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This study aims to investigate the effects of informational-based readiness and customer readiness (central route) as well as social influence (peripheral route) on the intention to use self-servic...

This study uses industrial upgrading (I), export development (E), and investment promotion (K), or collectively, the IEK model to identify critical indicators to asses competitiveness of the service industry in Taiwan. The proposed development and evaluation model for the service industry uses the DEMATEL (decision-making trial and evaluation laboratory) method to construct an influential relationship between the dimensions and criteria; a DEMATEL-based analytic network process (DANP) to obtain influential weights; and a modified Vlsekriterijumska Optimizacija I Kompromisno Resenje (m-VIKOR) method to assess gaps in industry development performance. It illuminates current industry demands and helps to improve service firms’ operations through fewer developmental gaps and stronger policy and strategy. Further, an influential network relationship map (INRM) and influential weights can unravel the complex assessment system and prioritise industry improvements. The results demonstrate that upgrades must occur first to the industry, then to promoting of investments, and lastly, in developing exports. Finally, the practical implications for managing service firms have been discussed.

Deploying static beacons to help sensor localization is a common approach in wireless sensor network. Alternatively, adopting a mobile beacon to travel along a specific trajectory and to broadcast its locations at specific points is more efficient. Therefore, path planning of the mobile beacon originally focused on finding shortest path length and minimum broadcast points, such as Scan and Hilbert, to decrease power consumption. More recently, path planning increasingly attempted to achieve higher accuracy, such as Double-scan and Z-scan. However, optimal trade-off between power efficiency and localization accuracy is yet to be achieved. In this paper, we designed $\Sigma $ -Scan which aims to reach high accuracy and coverage with short path length by combining the advantages of Scan and Z-scan. $\Sigma $ -Scan provides three kinds of unit to constitute arbitrary rectangles and minimize the minimum bounding rectangle. Compared to them, $\Sigma $ -Scan is more applicable, accessible and easy to implement. The simulation results show that compared to other previous approaches, $\Sigma $ -Scan has the highest ratio of accuracy and coverage to path length and also decreases the incidence of collinearity in time-priority trilateration (TPT).

In wireless sensor networks, it is important to use the best number of sensors to optimize the network and consider the key design and cost. Owing to the limited power of sensors, how controlling the state of the sensor through an automatic control algorithm and power-saving and efficient distribution of work have become important issues. However, sensor nodes are usually deployed in dangerous or inaccessible locations. Therefore, it is difficult and impractical to supply power to sensors through humans. In this study, we propose a high-reliability control algorithm with fast convergence and strong self-organization ability, called sensor activity control algorithm (SACA), which can efficiently control the number of sensors in the active state and extend their use time. SACA considers the relationship between the total number of inactive sensors and the target value and determines the state of the sensor in the next round. The data transmission technology of random access is used between the sensor and the base station; therefore, the sensor in the sleep state does not need to receive the feedback packet from the base station. The sensor can achieve true dormancy and power-saving effects. The experimental results show that SACA has fast convergence, strong self-organization capabilities, and power-saving advantages.

With the development of new technologies and applications, such as the Internet of Things, smart cities, 5G, and edge computing, traditional Internet Protocol-based (IP-based) networks have been exposed as having many problems. Information-Centric Networking (ICN), Named Data Networking (NDN), and Content-Centric Networking (CCN) are therefore proposed as an alternative for future networks. However, unlike IP-based networks, CCN routing is nondeterministic and difficult to optimize due to frequent in-network caching replacement. This paper presents a novel probe-based routing algorithm that explores real-time in-network caching to ensure the routing table storing the optimal paths to the nearest content provider is up-to-date. Effective probe-selections, Pending Interest Table (PIT) probe, and Forwarding Information Base (FIB) probe are discussed and analyzed by simulation with different performance measurements. Compared with the basic CCN, in terms of qualitative analysis, the additional computational overhead of our approach is O(*) and O(*) on processing interest packets and data packets, respectively. However, in terms of quantitative analysis, our approach reduces the number of timeout interests by 6% and the average response time by 0.6 s. Furthermore, although basic CCN and our approach belong to the same Quality of Service category, our approach outperforms basic CCN in terms of real values. Additionally, our probe-based approach performs better than * and *. Owing to speedup FIB updating by probes, our approach provides more reliable interest packet routing when accounting for router failures. In summary, the results demonstrate that compared to basic CCN, our probe-based routing approach raises FIB accuracy and reduces network congestion and response time, resulting in efficient routing., Comment: 16 pages, 9 figures, 3 tables

Most people associate Penghu with the roadside banquet, an iconic banquet in the area. Nevertheless, market demand for these banquets has lately fallen drastically, causing a severe impact on the industry. In this study, the critical factors influencing the choice to conduct roadside wedding banquets were investigated from the consumer's standpoint. Initially, on the basis of expert screening and literature study, 14 criteria covering five dimensions were developed. Subsequently, causal connections and impact weights were determined using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and DANP (DEMATEL-based analytic network process) models. The findings indicate that customers prioritise service quality in improvement initiatives. Dish quality, hygiene, and marketing are the most appreciated dimensions. Overall, customers place the highest significance on pricing. The industry is recommended to create online marketing platforms, raise the standard of wedding banquet services, combine the value chains of wedding services, and support the sustainable growth of the industry. The method used enriches existing theoretical approaches concerning roadside wedding banquets and is applicable in assisting wedding banquet providers' management in holding roadside weddings.

Due to the popularity of network applications, such as multimedia, online shopping, Internet of Things (IoT), and 5G, the contents cached in the routers are frequently replaced in Content-Centric Networking (CCN). Generally, cache miss causes numerous propagated packets to get the required content that deteriorates network congestion and delay the response time of consumers. Many caching strategies and routing policies were proposed to solve the problem. This paper presents an alternative solution by designing a query-based routing table update mechanism to increase the accuracy of routing tables. By adding an additional query content in interest packets, our approach real-time explores the cached content in routers and updated the routing table accordingly. This paper uses a general network simulator, ndnSIM, to compare basic CCN and our approach. The results show that our approach improves the response time of consumers and network congestion and is compatible with general forwarding strategies., 6 pages, 14 figures, conference. ISBN:978-1-7281-9256-7

In this study, we use six dimensions (i.e., firm strategy; government, structure, and rivalry; demand conditions; chance; factor conditions; and related/supporting industries) based on Porter’s diamond model to identify critical development indicators to enhance the competitiveness of Taiwan’s solar photovoltaic industry. Our proposed industry evaluation model uses the decision-making trial and evaluation laboratory (DEMATEL) technique to establish an influential relationship between indicators, the DEMATEL-based analytic network process to obtain influential weights, and the modified VIšekriterijumsko Kompromisno Rangiranje (VIKOR) method to assess industry development performance gaps. Through this analysis, current industry demands can be clarified, and operations of governments and photovoltaic firms can be improved by limiting developmental gaps and formulating robust policies and strategies. Furthermore, the influential network relationship map and influential weights can help explain the complex assessment system and suggest priorities for industry improvement. The results demonstrate that the six dimensions should be upgraded in the following order of priority: firm strategy; government, structure, and rivalry; demand conditions; chance; factor conditions; and related/supporting industries. We also discuss the implications for photovoltaic firm management.

Various investigations on wireless sensor networks (WSNs) have been conducted. However, only a few of these studies were related to quality of service (QoS). QoS control for WSNs is defined as the control of the number of active sensors. Effective control of the number of active sensors is important to prevent wastage in power resources and bandwidth as well as to satisfy the application demands. In a real network environment, controlling the number of active sensors is a difficult task because of the death and renewal of sensors, which cause frequent changes in the nodes. Two distributed algorithms were previously proposed for QoS control of WSNs. Two methods can be used for the effective control of sensor status: the Gur Game and ACK algorithms. This research proposed a new control algorithm called the Sensor Activity Control Strategy (SACS), which provides high-power saving of sensors. The SACS requires less time that the Gur Game and ACK algorithms to achieve the targets.

Background: Single dose hepatitis A virus (HAV) vaccine had been proven its efficacy in immunocompetent but not immunocompromised hosts. We aim to investigate the effectiveness of one dose versus 2 doses HAV vaccine among people living with HIV (PLHIV). Method: We conducted a 1:1 single center retrospective case–control study for PLHIV in Northern Taiwan. Case patients were those who received single dose HAV vaccine and controls were those who completed standard 2 doses HAV vaccine. Nationwide campaign of single dose HAV vaccine had been practiced for high risk population including PLHIV and those who had newly diagnosed sexually transmitted diseases. Results: During February 2016 and December 2017, 90 cases received single dose HAV vaccine provided while the other 90 age-matched controls received 2 doses vaccine were enrolled. We found more injection drug users (22.22% vs. 1.11%, p

This study constructed a model using multiple attribute decision making (MADM) to evaluate the successful marketing of products from vendor partners at an on-campus training store. We used the 5Ps of marketing (people, product, price, promotion, and place) as the research framework and applied the fuzzy analytic hierarchy process (FAHP) to calculate the weight of each evaluation criterion. A gap analysis was performed on each evaluation criterion, using the modified VlseKriterijumska Optimizcija I Kaompromisno Resenje (modified VIKOR) to identify the important factors that students at an on-campus training store should prioritize for improvement when formulating marketing strategies from vendor partners. The results indicatae that when collaborating with the on-campus training store, vendor partners placed the greatest emphasis on promotion and the least on people. The results indicatae that when collaborating with the on-campus training store, vendor partners placed the greatest emphasis on promotion and the least on people. The evaluation dimensions were ranked according to their weights in descending order as follows: promotion(0.279), product(0.239), price(0.179), place(0.157), and people(0.143). Based on the overall weights of all evaluation criteria featured in present study, vendor partners placed the top five greatest emphasis on product quality and safety assurance(0.088), social media marketing(0.078), price promotion activities(0.071), product uniqueness(0.069), as well as brand advertising and promotion(0.066). The current research provides practical and appropriate strategies for planning successfully product marketing strategies from vendor partners, and further helping vendor partners increase product exposure and attract consumers to purchase their products at the on-campus training store. The findings may help vendor partners increase and improve product exposure and attract consumers to purchase their products.

Over time, correctional institutions have actively implemented innovations in prison administration. Most correctional institutions constitute conservative and closed societies. Generally, people do not have sufficient knowledge or understanding of penal correction. Therefore, it is difficult but necessary to break the stereotyped impression people hold about correctional institutions, actively integrate them into society, and encourage people to assume a more humane attitude towards correctional institutions. We conducted an inductive analysis of Penghu Prison administered by the Agency of Corrections, Ministry of Justice, Taiwan. After creating evaluation indices for the development of more humane correctional institutions, we conducted a survey to classify the evaluation indices into criteria under the four major evaluation dimensions. Using DEMATEL, we determined the causal relationship among different evaluation indicators and generated an influential network relation map about the evaluation criteria. Using DANP, we subsequently calculated weightings for the influence of each criterion. Finally, using the modified VIKOR, we conducted a gap analysis. The results of this study can be used to build a more positive and human reputation for correctional institutions and enable their sustainable creation.

On-site multiview video viewing system can provide people with more immersive viewing experience on various live video shows, such as sports and concerts. To address the high computational complexity of multi-view video coding, several researches have worked on fast motion estimation, fast disparity estimation, and/or fast mode decision. In this paper, we propose a low-complexity encoding scheme that can further exploit inter-view dependencies and depth information. A fast mode selection with threshold-based early termination is presented to select a minimal set of mode candidates according to the reference macroblocks of neighbouring views and expedite the mode decision process. Further, this study also demonstrates that disparity estimation computation and search ranges of both motion estimation and disparity estimation can be significantly refined by using inter-view dependencies and depth information. The experimental results show that the proposed encoding method reduces the coding time by 80% with a negligible PSNR loss.

The recent emergence and rapid growth of new financial services by financial technology (Fintech) companies have driven banking institutions towards operational innovation in order to gain sustainable competitive advantage. This study aims to conduct an in-depth investigation of the banking sector in response to the challenges brought by Fintech startups. Based on the service innovation theory, we propose a novel hybrid multiple criteria decision-making method (MCDM) to evaluate service innovation strategies for improving the sustainability of China&rsquo, s banking industry during the Fintech revolution. A six-dimensional model comprising 20 sub-criteria is constructed and both the decision making trial and evaluation laboratory (DEMATEL) technique and DEMATEL-based analytic network process (DANP) are used to explore interrelationships among the indices and their related weights. Finally, the modified VI&scaron, ekriterijumsko KOmpromisno Rangiranje (VIKOR) method is employed to evaluate performance gaps in the four major types of commercial banks in China&mdash, state-owned, joint-stock, city commercial banks, and other credit cooperatives&mdash, in the field of service innovation. The improvement priorities, ranked from highest to lowest, are new business partners, new service concepts, organizational innovation, technological innovation, new customer interactions, and new revenue models. These results will provide strategies for the sustainable development of China&rsquo, s banking industry and the implementation of changes in response to the impact of the Fintech revolution.

This study investigates the key development evaluation indicators of the TV game console industry based on the four dimensions of Saaty's ‘benefits, opportunity, costs, and risks’ model. The hybrid multiple attribute decision-making model is employed to create a new evaluation approach. The proposed hybrid model uses the decision-making trial and evaluation laboratory (DEMATEL) to determine the influential relationships among dimensions and criteria, the DEMATEL-based analytic network process to obtain influential weights, and the modified VlseKriterijumska Optimizacija I Kompromisno Resenje method to assess performance gaps in industry development. The influential network relationship map and influence weights help TV game console firms' managers understand the industry's complex assessment system and set priorities for improving the industry's performance. The results of the study indicate that the improvement priorities of the model's dimensions have the following order: benefits, opportunity, costs, and risks. Several implications for the industry's management are also discussed.

The aim of this study is to find the critical factors that influence Taiwan's national museum business performance based on its curators’ views. The study explored the causal relationships among the criteria that emerged in the study and of each sub-criteria. Since developing a business strategy is a multiple-criteria decision-making (MCDM) problem, this study adopted the causal-effect model of decision-making trial and evaluation laboratory (DEMATEL) technique. The DEMATEL technique simplifies and visualizes the interrelationships among decision-making criteria. The study identified four core criteria – benefits, opportunity, costs, and risks, as key influencers in the national museum business performance. Each key criteria was supported by a set of sub-criteria which, when considered together, produced an influential network relations map. The results of this study provided Taiwan's national museum curators with an idea-based understanding of how to create business and marketing strategies that could enhance exhibition features, experience activities, and facilities that could be linked to satisfaction of visitors’ desires and offer a potentials strategy for assessing likelihood of return visits.

Numerous approaches have been proposed to solve shortest path query problems (SPQPs) in either static or time-dependent spatial networks; however, these approaches are neither appropriately nor efficiently used to find the nearest shelter with fastest paths in disaster evacuations. In disasters, segments of a path computed and saved as the fastest might become impassable. The nearest shelter differs for people depending on their locations and can also change on the basis of an unpredictable and highly dynamic edge cost (e.g., maximum passable vehicle speed), which is influenced by disaster events. To solve this problem, this paper proposes a dynamic network model, which is called an event-dependent network, to represent a spatial network in a disaster. Effective approaches using multiple algorithms are proposed for on-the-fly computation of the nearest shelter and fastest paths in a disaster. A distributed system consisting of a server and multiple mobile clients using our approaches is presented for navigating the fastest paths for people to evacuate a disaster area. Real-world maps, such as a map of California, were used in our experiments. The results revealed that our approaches require less than 2 ms to find a new nearest shelter and its fastest paths, which is faster than other approaches for solving the fastest-path problem.

The ACM Symposium on User Interface Software and Technology (UIST) is the premier forum for innovations in human-computer interfaces. This year, we curated more than 40 demonstrations, aimed to allow conference attendees to look, touch, and witness new and inspiring technologies live. Here, we selected four projects that showcase the very best in the community. Chris Harrison and Nicolai Marquardt, UIST 2015 Demo Committee Chairs

Management strategies play an important role in enhancing the competitive advantage and sustainable development of national museums. The purpose of this study is to focus on evaluating the management strategies of national museums to reduce the gaps in visitor satisfaction that are caused by the interdependence and feedback problems of the so-called BOCR dimensions (namely, benefits, opportunity, costs, and risks) and 24 criteria. This study creates a hybrid competitive advantage multiple-criteria decision-making model for national museums by integrating the decision-making trial and evaluation laboratory-based analytic network process and modified VIKOR (VlšeKriterijumska Optimizacija I Kompromisno Resenje) techniques to solve the problems. We consider five different types of national museums to illustrate how the proposed new evaluation model enhances the competitive advantage of national museums. Our results provide national museum curators with the knowledge and understanding to create promotional and marketing strategies that reduce the gaps in dimensions and criteria to satisfy visitors’ needs and to enhance their competitive advantage.

Search and rescue (SAR) is to find and help people who are injured or in danger in difficult environments, e.g., mountain, marine or rainforest. The time of SAR should be short to minimize the damage and increase the survival rate of the person. However, SAR is time-consuming, especially for searching missing people in large scale area. Many techniques including unmanned aerial vehicle, robot, and auto-car, are applied to assist SAR nowadays. This paper proposes a map model with a greedy path planning algorithm to guide searchers efficiently find missing people faster and take UAV searching as a scenario.

We propose CatAR, a novel stereoscopic augmented reality (AR) cataract surgery training system. It provides dexterous instrument tracking ability using a specially designed infrared optical system with 2 cameras and 1 reflective marker. The tracking accuracy on the instrument tip is 20 µm, much higher than previous simulators. Moreover, our system allows trainees to use and to see real surgical instruments while practicing. Five training modules with 31 parameters were designed and 28 participants were enrolled to conduct efficacy and validity tests. The results revealed significant differences between novice and experienced surgeons. Improvements in surgical skills after practicing with CatAR were also significant.

We demonstrate a managed edge computing platform for Internet-of-Things (IoT) devices, which supports dynamic deployment of virtualized containers running distributed analytics. We build a model city, and install multiple Raspberry Pis as minions, and a mini PC as the master. Through the web dashboard on the master, we show how users can remotely monitor, manage, and upgrade the IoT analytics and devices. Multiple concrete IoT analytics, namely: (i) air quality monitor, (ii) sound classifier, and (iii) image recognizer are demonstrated. Several sample measurements on deployment speed, Quality-of- Service (QoS) achievements, and event-driven mechanisms are also carried out on the testbed.

In this paper, we design and implement an Internet-of-Things (IoT) based platform for developing cities using environmental sensing as driving application with a set of air quality sensors that periodically upload sensor data to the cloud. Ubiquitous and free WiFi access is unavailable in most developing cities; IoT deployments must leverage 3G cellular connections that are expensive and metered. In order to best utilize the limited 3G data plan, we envision two adaptation strategies to drive sensing and sensemaking. The first technique is an infrastructure-level adaptation approach where we adjust sensing intervals of periodic sensors so that the data volume remains bounded within the plan. The second approach is at the information-level where application-specific analytics are deployed on board devices (or the edge) through container technologies (Docker and Kubernetes); the use case focuses on multimedia sensors that process captured raw information to lower volume semantic data that is communicated. This approach is implemented through the EnviroSCALE (Environmental Sensing and Community Alert Network) platform, an inexpensive Raspberry Pi based environmental sensing system that periodically publishes sensor data over a 3G connection with a limited data plan. We outline our deployment experience of EnviroSCALE in Dhaka city, the capital of Bangladesh. For information-level adaptation, we enhanced EnviroSCALE with Docker containers with rich media analytics, along Kubernetes for provisioning IoT devices and deploying the Docker images. To limit data communication overhead, the Docker images are preloaded in the board but a small footprint of analytic code is transferred whenever required. Our experiment results demonstrate the practicality of adaptive sensing and triggering rich sensing analytics via user-specified criteria, even over constrained data connections.

Modern IoT analytics are computational and data intensive. Existing analytics are mostly hosted in cloud data centers, and may suffer from high latency, network congestion, and privacy issues. In this paper, we design, implement, and evaluate a fog computing platform that runs analytics in a distributed way on multiple devices, including IoT devices, edge servers, and data-center servers. We focus on the core optimization problem: making deployment decisions to maximize the number of satisfied IoT analytics. We carefully formulate the deployment problem and design an efficient algorithm, named SSE, to solve it. Moreover, we conduct a detailed measurement study to derive system models of the IoT analytics based on diverse QoS levels and heterogeneous devices to facilitate the optimal deployment decisions. We implement a testbed to conduct experiments, which show that the system models achieve reasonably good accuracy. More importantly, 100% of the deployed IoT analytics satisfy the QoS targets. We also conduct extensive simulations for larger-scale scenarios. The simulation results reveal that our SSE algorithm outperforms a state-of-the-art algorithm by up to 89.4% and 168.3% in terms of the number of satisfied IoT analytics and active devices. In addition, our SSE algorithm reduces CPU, RAM, and network resource consumptions by 18.4%, 12.7%, and 898.3%, respectively, and terminates in polynomial time.

This study aims to construct the business model for firms in Taiwan's e-book industry.This study applied a hybrid fuzzy MCDM approach including FAHP, VIKOR, GRA, and TOPSIS to achieve the aim.This study provides a complete analysis of e-book business model selection. This study evaluates an appropriate business model for e-book firms in Taiwan. We apply expert questionnaires to calculate the weights, which contain five criteria, namely, business strategy (BS), finance characteristics (FC), market characteristics (MC), quality measurements of product and service (QS) and implementation (IM) (BCCSM) for the capability of developing the firms' business. Then, a fuzzy analytic hierarchy process (FAHP) is adopted to explore the weights of indices, and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), grey relation analysis (GRA), and the technique for order preference by similarity to ideal solution (TOPSIS) are utilized to rank the three alternatives. The results show that a single brand is the best e-book firms' business strategy that is simultaneously integrated by content, platforms, and devices, while the top two weights of the evaluation criteria are the business strategy and market characteristics to enable firms to develop an appropriate e-book business model.

A typical disaster surveillance and early warning system often needs to make timely and critically important preparedness decisions before disasters strike. When crowdsourcing observational data from people to enhance its sensor coverage, the system must be able to make effective use of volunteers, guide them during their exploration of the threatened area, and process reports from them in real time to extract decision support information of sufficiently good quality. This article focuses on the participant selection problem (PSP) which the system must solve in order to select participants from available volunteers given the benefits and costs of deploying them. The PSP-Greedy (PSP-G) algorithm proposed is known to be a near-optimal solution with a small fraction of execution time when compared with well-known optimization methods. The article describes an implementation of the PSP-G algorithm and the integration of the resultant PSP-G module into the Ushahidi platform. Performance data from two case studies based on Haiti Earthquake, 2010, and Typhoon Morakot, 2009, also described here, clearly show that PSP-G is a general practical solution.

On-site VR360 video viewing system can provide people with immersive viewing experience on various live activities, such as basketball, baseball, and concert. The main challenge of developing on-site VR360 viewing system is to design extremely low-latency algorithms and system implementation. This article will present a GPU-based implementation of on-site VR360 video viewing system with 4K resolution and 60 fps frame rates. The experimental results show that the latency is nearly 160 ms without network and display delay, and the overall latency is nearly 500 ms.

With the advancement of health care and technology, establishing a highly fast, accurate, and efficient medical system has been an ultimate goal for a country or a government. When a large-scale disaster with dozens of patients or more occurs, it often results in short supply of immediate medical resources to the patients, such as long waiting time for ambulances that causes a patient's death. In this paper, we establish a hybrid resource allocation system to model the problem as a hard real-time problem, and analyze two current scheduling algorithms for real-time system, namely clock driven (e.g. hospital assignment for emergency patients, HAEP) and priority driven (e.g. earliest deadline first, EDF). We further design a medical resource allocation algorithm which aims to reduce the computing time of the system and improve the accuracy of the allocation of medical resources. By making it suitable for large-scale disaster, it is hoped to be adopted by the disaster relief personnel and the government.

In a robot teleoperation system, humans operate and respond by relying on the content displayed on the video screen. However, unavoidable delays, such as network transmission, cause asynchronous communication between humans and robot and further result in human operation errors such as redundant or duplicate operations or even wrong commands. Users of consumer electronics in daily life are different from users in large-scale industry as they may not be experts or even trained to use the robots leading to new challenges in developing home-used robots. In this study, a module-based remote control system is designed to illustrate the possible human operation errors caused by transmission delay of transmission control protocol/Internet protocol–based network and to provide a user-friendly framework for simulating solutions to improve human errors. This is followed by proposing two target prediction methods, namely the target area predictor and stop position predictor, to decrease the human errors. The experimental results show that target area predictor and stop position predictor effectively improve the performance by reducing task completion time and object trajectory errors by 60% and 36%, respectively.

In robot teleoperation system, human operates and respond rely on what the video screen displays. However, unavoidable delays, such as network transmission, cause asynchronous communication between human and robot and further result in human operation errors, such as redundant or duplicate operations or even wrong commands. Different from users in large-scale industry, users in daily life may not be experts or even trained to use the robots so there are new challenges for developing home-used robots. In this paper, we design a module-based remote control system to not only illustrate the possible human operation errors caused by network delay but also provide a user-friendly framework for simulating solutions to improve human errors. We then proposed two target prediction methods, Target Area Predictor (TAP) and Stop Position Predictor (SPP), to decrease these human errors. The experiment results show that TAP and SPP effectively improve the performance by reducing 60% task completion time and 36% contour errors.

Photographs are a convenient way to collect data from a disaster, but the disadvantage is that the file size of the photo is much larger than that of the plain text, resulting in time delays, data transfers, and power consumption in mobile devices. Besides, photo covers many of the “non-essential information”. Our goal is to get the location of disaster events by using GPS location of the camera with the depth of field estimation of disaster event location to convert the photo information into text packets and then based on the network status to decide the file transfer type. When the network is in good condition and the transmission cost is low, the original image is transmitted directly. When the network status is poor or the transmission cost is high, the data and information are converted to text first so that the packet data is reduced.

Underwater Wireless Sensor Networks (UWSNs) are widely used to collect data in the marine environment. Location and time are essential aspects when sensors collect data, particularly in the case of location-aware data. Many studies on terrestrial sensor networks consider sensor locations as the locations where data is collected and focus on sensor positioning when sensors are fixed. However, underwater sensors are mobile networks and the sensor locations change continuously. Localization schemes designed for static sensor networks need to run periodically to update locations and consume considerable sensor power and increase the communication overhead; hence, they cannot be applied to UWSNs. This paper presents a hybrid localization approach with data-location correction, called Data Localization Correction Approach (DLCA), which positions data without additional communication overhead and power consumption on sensors. Without loss of generality, we simulate the ocean environment based on a kinematic model and meandering current mobility model and conduct extensive simulations. Our results show that DLCA can significantly reduce communication costs, while maintaining relatively high localization accuracy.

Many disaster warning and response systems can improve their surveillance coverage of the threatened area by supplementing in situ and remote physical sensor data with crowdsourced human sensor data captured and sent by people in the area. This paper presents fusion methods which enable a crowdsourcing enhanced system to use human sensor data and physical sensor data synergistically to improve its sensor coverage and the quality of its decisions. The methods are built on results of classical statistical detection and estimation theory and use value fusion and decision fusion of human sensor data and physical sensor data in a coherent way. They are the building blocks of a central fusion unit in a crowdsourcing support system for disaster surveillance and early warning applications.

Lithium (Li) is a fluid-mobile element and δ 7 Li in secondary deposits represents an excellent proxy for silicate weathering and authigenic mineral formation. The soil samples from 1205 to 1295 cm in the Weinan profile, one of the best developed loess-paleosol sequences covering the last glacial–interglacial climatic cycle, were collected and chemically separated into detritus and carbonate fractions for subsequent analyses of Li, δ 7 Li, major and trace elements. Other desert specimens (i.e., Qaidam Desert, Tengger Desert, Badain Juran Desert and Taklimakan Desert) near the Chinese Loess Plateau (CLP) and various standard clays were analyzed for assisting provenance determination. The Li and δ 7 Li distributions in the detritus are rather homogeneous, 1.4–2.0 μg/g and +2.5‰ to +4.7‰, respectively, compared with the carbonate fraction. The detrital δ 7 Li varies systematically with magnetic susceptibility and grain size changes, reflecting significant Li isotopic variation associated with sources and mineralogy of detrital material. On the other hand, Li and δ 7 Li in carbonates show large changes, 781–963 ng/g and −4.1‰ to +10.2‰, respectively. These carbonate δ 7 Li correlated well with the estimated index of chemical weathering, as a result of Li mobilization and soil formation during chemical weathering.

Monitoring extreme values (maximum or minimum) is important to many applications in wireless sensor networks. A previous work, called Hierarchy Adaptive Threshold (HAT), proposed a tree-based structure to distribute queries efficiently and filter out the unnecessary data updates that are not extreme values. In this paper, a data reduction algorithm is presented to reduce energy consumption of the HAT due to network transmission. The proposed method utilizes historical information of extreme values and their corresponding node ID to adjust the reporting rate of sensors properly and eases the burden of the parent of extreme nodes by balancing the packets from extreme nodes to all their possible parents. We evaluate the performance of the proposed algorithm by NS-2 network simulator and real-world data traces. The results indicate that the overall network packets are reduced to 80% with 1% data error in comparison with HAT.

Several applications, such as smart cities, smart homes and smart hospitals adopt Internet of Things (IoT) networks to collect data from IoT devices. The incredible growing speed of the number of IoT devices congests the networks and the large amount of data, which are streamed to data centers for further analysis, overload the data centers. In this paper, we implement a fog computing platform that leverages end devices, edge networks, and data centers to serve the IoT applications. In this paper, we focus on implementing a fog computing platform, which dynamically pushes programs to the devices. The programs pushed to the devices pre-process the data before transmitting them over the Internet, which reduces the network traffic and the load of data centers. We survey the existing platforms and virtualization technologies, and leverage them to implement the fog computing platform. Moreover, we formulate a deployment problem of the programs. We propose an efficient heuristic deployment algorithm to solve the problem. We also implement an optimal algorithm for comparisons. We conduct experiments with a real testbed to evaluate our algorithms and fog computing platform. The proposed algorithm shows near-optimal performance, which only deviates from optimal algorithm by at most 2% in terms of satisfied requests. Moreover, the proposed algorithm runs in real-time, and is scalable. More precisely, it computes 1000 requests with 500 devices in < 2 seconds. Last, the implemented fog computing platform results in real-time deployment speed: it deploys 20 requests < 10 seconds.

This paper proposes a novel temporal distortion cost (TDC) for sample adaptive offset (SAO) filter in High Efficiency Video Coding. The TDC addresses the images with temporal distortion by evaluating the errors between pre/post-SAO samples and raw samples in continuous frames. Due to high computational complexity of TDC, we further propose a low-complexity TDC (L-TDC) to estimate the value of TDC by a difference classification scheme and a rate-distortion decision scheme. We also contribute to deriving a spatio-temporal distortion cost (STDC) and a low-complexity STDC (L-STDC) by incorporating a spatial distortion cost into the proposed TDCs. Simulation results show that, compared to the classical SAO filter, the proposed SAO filters can save up to around 10% ∼ 15% bitrates under the same flicker measure, and achieve better flicker measure with over 10% improvement in average under the same bitrate. Some valuable threshold analyses are also provided.

Users always got bad experiences while using the general virtual reality head-mounted displays (HMDs) because of the low pixel density through optical lenses. For this reason, the narrow field-of-view (FoV) and high pixel density are the main goals we are going to pursue in the near-field video see-through augmented reality (AR) applications with sophisticated operations, such as the biological observation with AR microscope (e.g. Scope+ [Huang et al. 2015]), the AR surgery simulation, and telescope applications. Therefore with high resolution to see tiny objects clearly is the most important concern in this paper.

High Efficiency Video Coding HEVC Screen Content Coding SCC is under standardizing for the screen-captured contents. Because many areas are composed of the texts and lines featuring non-smooth textures, the traditional intra prediction is not suitable for those areas. Therefore, this paper proposes a neighboring sample prediction coding NSPC for HEVC SCC, which represents the samples of the coding unit CU by the indexes of samples selected from the neighboring CUs. A unified sample selection scheme USSS based on a neighboring sample list is also proposed to determine the priority ordering of the selected neighboring samples. The index coding with a single sample index skipping method is also designed for coding the converted indexes. Different NSPC signaling methods are evaluated under the common test condition for HEVC SCC, and the results show that the NSPC improves the Bjontegaard-Delta bitrate saving of Screen Content Coding Test Model 2.0 up to 1.0i��%.