Fog computing-based approximate spatial keyword queries with numeric attributes in IoV

Due to the popularity of on-board geographic devices, a large number of spatial-textual objects are generated in Internet of Vehicles (IoV). This development calls for Approximate Spatial Keyword Queries with numeric Attributes in IoV (ASKIV), which takes into account the locations, textual descriptions, and numeric attributes of spatial-textual objects. Considering huge amounts of objects involved in the query processing, this paper comes up with the ideal of utilizing vehicles as fog-computing resource, and proposes the network structure called FCV, and based on which the fog-based Top-k ASKIV query is explored and formulated. In order to effectively support network distance pruning, textual semantic pruning, and numerical attribute pruning simultaneously, a two-level spatial-textual hybrid index STAG-tree is designed. Based on STAG-tree, an efficient Top-k ASKIV query processing algorithm is presented. Simulation results show that, our STAG-based approach is about 1.87x (17.1x, resp.) faster in search time than the compared ILM (DBM, resp.) method, and our approach is scalable.University of Derb

Edge intelligence-enabled cyber-physical systems

With the advent of the Internet of everything era, people's demand for intelligent Internet of Things (IoT) devices is steadily increasing. A more intelligent cyber-physical system (CPS) is needed to meet the diverse business requirements of users, such as ultra-reliable low-latency communication, high quality of services (QoS), and quality of experience (QoE). Edge intelligence (EI) is recognized by academia and industry as one of the key emerging technologies for the CPS, which provides the ability to analyze data at the edge rather than sending it to the cloud for analysis, and will be a key enabler to realize a world of a trillion hyperconnected smart sensing devices.As a distributed intelligent computing paradigm in which computation is largely or completely performed at distributed nodes, EI provides for the rapid development of artificial intelligence (AI) and edge computing resources to support real-time insight and analysis for applications in CPS, which brings memory, computing power and processing ability closer to the location where it is needed, reduces the volumes of data that must be moved, the consequent traffic, and the distance the data must travel. As an emerging intelligent computing paradigm, EI can accelerate content delivery and improve the QoS of applications, which is attracting more and more research attentions from academia and industry because of its advantages in throughput, delay, network scalability and intelligence in CPS.The guest editors would like to thank all the authors and the reviewers for their hard work and contributions in helping to organize this special issue. They also would like to express their heartfelt gratitude to the Editor-in-Chief, Prof. David W. Walker, for giving us this great opportunity, and the members of the Editorial Staff for their support during the process.Scopu

Oral Morphine Versus Ibuprofen Administered at Home for Postoperative Orthopedic Pain in Children: a Randomized Controlled Trial

BACKGROUND: Oral morphine for postoperative pain after minor pediatric surgery, while increasingly popular, is not supported by evidence. We evaluated whether oral morphine was superior to ibuprofen for at-home management of children\u27s postoperative pain. METHODS: We conducted a randomized superiority trial comparing oral morphine (0.5 mg/kg) with ibuprofen (10 mg/kg) in children 5 to 17 years of age who had undergone minor outpatient orthopedic surgery (June 2013 to September 2016). Participants took up to 8 doses of the intervention drug every 6 hours as needed for pain at home. The primary outcome was pain, according to the Faces Pain Scale - Revised, for the first dose. Secondary outcomes included additional analgesic requirements, adverse effects, unplanned health care visits and pain scores for doses 2 to 8. RESULTS: We analyzed data for 77 participants in each of the morphine and ibuprofen groups. Both interventions decreased pain scores with no difference in efficacy. The median difference in pain score before and after the first dose of medication was 1 (interquartile range 0-1) for both morphine and ibuprofen ( INTERPRETATION: Morphine was not superior to ibuprofen, and both drugs decreased pain with no apparent difference in efficacy. Morphine was associated with significantly more adverse effects, which suggests that ibuprofen is a better first-line option after minor surgery. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT01686802

A cascade learning approach for automated detection of locomotive speed sensor using imbalanced data in ITS

Automatic and intelligent railway locomotive inspection and maintenance are fundamental issues in high-speed rail applications and intelligent transportation system (ITS). Traditional locomotive equipment inspection is carried out manually on-site by workers, and the task is exhausting, cumbersome, and unsafe. Based on computer vision and machine learning, this paper presents an approach to the automatic detection of the locomotive speed sensor equipment, an important device in locomotives. Challenges to the detection of speed sensor mainly concerns complex background, motion blur, muddy noise, and variable shapes. In this paper, a cascade learning framework is proposed, which includes two learning stages: target localization and speed sensor detection, to reduce the complexity of the research object and solve the imbalance of samples. In the first stage, histogram of oriented gradient feature and support vector machine (HOG-SVM) model is used for multi-scale detection. Then, an improved LeNet-5 model is adopted in the second stage. To solve the problem of the imbalance of positive and negative samples of speed sensor, a combination strategy which draws on four individual classifiers is designed to construct an ensemble of classifier for recognition, and the results of three different algorithms are compared. The experimental results demonstrate that our approach is effective and robust with respect to changes in speed sensor patterns for robust equipment identification.N/

Gigahertz-rate-switchable wavefront shaping through integration of metasurfaces with photonic integrated circuit

Achieving spatiotemporal control of light at high-speeds presents immense possibilities for various applications in communication, computation, metrology, and sensing. The integration of subwavelength metasurfaces and optical waveguides offers a promising approach to manipulate light across multiple degrees of freedom at high-speed in compact photonic integrated circuit (PICs) devices. Here, we demonstrate a gigahertz-rate-switchable wavefront shaping by integrating metasurface, lithium niobite on insulator (LNOI) photonic waveguide and electrodes within a PIC device. As proofs of concept, we showcase the generation of a focus beam with reconfigurable arbitrary polarizations, switchable focusing with lateral focal positions and focal length, orbital angular momentum light beams (OAMs) as well as Bessel beams. Our measurements indicate modulation speeds of up to gigahertz rate. This integrated platform offers a versatile and efficient means of controlling light field at high-speed within a compact system, paving the way for potential applications in optical communication, computation, sensing, and imaging

Information Engineering and Applications

The International Conference on Information Engineering and Applications (IEA) 2011 will be held on October 21-24, 2011, in Chongqing, China. It is organized by Chongqing Normal University, Chongqing University, Shanghai Jiao Tong University, Nanyang Technological University, the University of Michigan, Chongqing University of Arts and Sciences, and sponsored by the National Natural Science Foundation of China. The objective of IEA 2011 is to facilitate an exchange of information on best practices for the latest research advances in the area of information engineering and intelligence applications, which mainly includes computer science and engineering, informatics, communications and control, electrical engineering, information computing, business intelligence and management. IEA 2011 will provide a forum for engineers and scientists in academia, industry, and government to address the most innovative research and development including technical challenges, social and economic issues, and to present and discuss their ideas, results, work in progress and experience in all aspects of advanced information and business intelligence. The conference scope will place emphases on original work on identifying new research and development challenges and developing new techniques and algorithms in information and business intelligence. Original research papers in information and business intelligence on new theories, methods, algorithms, experimental and applied researches are solicited. Authors will be invited to submit complete unpublished papers, which are not under review in any other conference or journal. All tracks are open to both research and industry contributions. The accepted papers will be collected into two volumes of proceedings

A Monte Carlo localization method based on differential evolution optimization applied into economic forecasting in mobile wireless sensor networks

Abstract The localization of sensor node is an essential problem for many economic forecasting applications in wireless sensor networks. Considering that the mobile sensors change their locations frequently over time, Monte Carlo localization algorithm utilizes the moving characteristics of nodes and employs the probability distribution function (PDF) in the previous time slot to estimate the current location by using a weighted particle filter. However, it also has the problem of insufficient number of valid samples, which further affects the node’s localization accuracy. In this paper, differential evolution method is introduced into the Monte Carlo localization algorithm. The sample weight is taken as the objective function, and differential evolution algorithm is implemented in sample stage. Finally, the node position is estimated by making the sample close to the actual location of the node instead of being filtered out. The simulation results demonstrate that the proposed algorithm provides a better position estimation with less localization error