Retransmission schemes for streaming internet multimedia

Internet of Vehicles (IoV), which plays a significantly important role in smart future cities, has become current hot research topics. However, the high heterogeneous nature of IoV has brought many new challenges such as low network performance and difficult network management for IoV. Software-defined networking enables the efficient solution of these problem. This article studies the measurement and analysis technology for software-defined networking of IoV. A new software-defined networking-based IoV heterogeneous networking measurement framework is proposed to build software-defined networking of IoV. We propose a performance measurement and analysis method to measure and characterize its performance. The performance indexes and measure methods about the delay, loss, throughput, delay jitter is in detail derived. The switch selection mechanism is proposed to establish optimal measurement points of advantage. The packet sampling process is presented to quickly obtain the needed measurement information from massive traffic flows. To validate our measurement method and fairly characterize its measurement performance for different controllers, we conduct massive simulation experiments to systematically analyze and compare current famous controllers. In such a case, we provide more comprehensive, systematic measurement analysis for application in software-defined networking of IoV. Experiments results show that our measurement approach is feasible and effective.

Modeling switches for performance management has somewhat different objectives as compared to modeling for switch design and performance evaluation. While, in general, a larger modeling error is tolerable in performance management, it has to be fast, consistent and handle general traffic inputs and server types. As a preliminary step to meeting these goals, we are modeling a shared memory ATM Switch. In this model a heterogeneous set of traffic sources is approximated by a set of identical Markov modulated rate processes. The main purpose of using this model is that we get closed form or fast numerical solutions for packet loss and the delay distribution. This is very important due to the computational complexity and large round-off errors in computation when a large number of heterogeneous burst sources have to be modeled. The performance measures we are then able to obtain for this switch are average delay, delay jitter (95 percentile delay) and loss probability. Specifically, our approach is based on an approximation of actual input sources by suitable On Off sources. Actual sources are composed of a superposition of many heterogeneous sources, each source with a different peak rate, load and first two moments of the burst period arid silent period. By matching moments of their arrival rate and correlations we can find a set of identical sources with approximately the same statistics as the original sources. We can then find the queue length distribution and delay distribution. Several numerical examples will be presented to illustrate this modeling technique and to validate performance result

Purpose – The purpose of this paper is to help build up a relevant database for mapping technical and financial effectiveness of production in order to make cost‐effective maintenance decisions. Design/methodology/approach – A theoretical model is developed based on past research and experience adopting a holistic systems approach on the production. A case study, which includes databases of two maintenance‐used software programs, verifies the potential of applying the model. Findings – The main result achieved is a model for identifying relevant data required for accurate problem tracing and localisation within maintenance and production processes using a top down approach. The main conclusions are integration of IT and data resources within the enterprise is needed for developing a holistic view of the production process and a well‐formulated and documented procedure of data identification will ensure that the data can be traced back to root sources and in this way we can support the work of continuous cost‐effective improvement by eliminating root causes of problems at an early stage. Research limitations/implications – Further model verification by industrial case studies would be of interest. Practical implications – The holistic approach and the model presented are applicable especially in capital intensive industries, where maintenance budget is not negligible and the amount of data to process is large. By structuring the data need and data identification process relevant performance measures will be monitored and advanced maintenance concepts can be applied. Originality/value – By applying the proposed model in industry, the data identification process itself and not the data contents is necessary to be standardised and structured. It shifts the focus of the quality aspect from just data level to both data and data collection level. The performance measures will therefore not be chosen depending on what the IT applications can provide in first hand, but upon what is needed for cost‐effective mapping, analysis, following up and assessment of maintenance performance.

Maltreatment can influence normative development and negatively impact emotional, behavioral, and social functioning in youth. As a result, it is not surprising that maltreated youth, as compared to non-maltreated youth, tend to underperform academically. Research on the academic performance of maltreated youth has increased over the last decade and several review papers have been published in this area. While the conclusions of these review articles have been that maltreated youth are at greater risk for academic deficits as compared to their non-maltreated peers, there are several conflicting findings within the literature that make it difficult to determine if or to what extent maltreated youth may demonstrate academic difficulty. Using a multilevel, structural equation model meta-analysis technique, the current study sought to provide a quantitative synthesis of the literature by examining the mean difference between maltreated and non-maltreated youth on measures of academic performance. Moreover, the current study also examined group differences between academic subject and maltreatment type. A total of 72 effect sizes were extracted from 32 studies that met inclusion criteria. Results demonstrated an overall negative, medium effect size, such that maltreated youth tended to perform slightly greater than half a standard deviation below non-maltreated youth on measures of academic performance. Moderation analyses suggest deficits may be greater on measures of general academic performance, as compared to language arts measures. No differences were observed for maltreatment type. These findings highlight the need for increased focus on academic difficulties among maltreated youth.

The main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length.Graphical

The management of a supply chain is both an offensive and defensive weapon that organisations can use to increase their competitive edge and capture a larger share of the market. In management science and supply chain management, multi-criteria decision making techniques have been used to solve a range of real-world problems. The problem is that many, if not most, companies in South Africa either do not have the required skills to use these decision-making techniques to improve or re-configure their supply chain, or they do not have a complete data set with which to model it effectively. In order to manage supply chains effectively, organisations at the very least need feedback on the performance of their entire supply chain. In this article, generic supply chain performance measures were used and a theoretical or empirical model was developed for the performance measurement of a national egg producer’s supply chain. It focused on a managerial program for the identification and management of their supply chain with recommendations for applying a measurement model. The overall performance of the supply chain as well as the five different performance attributes was presented to management in a dashboard format. This article could be used as a basis for future studies of supply chain performance measurement and the model could be used as a foundation for developing an improved version, not only for the egg industry, but for other industries as well.

A study was conducted on the effects of 72 hours of sleep loss and modified continuous operations on performance and psychological variables. This paper presents the results of self-report data of 12 subjects for the following psychological variables: sleepiness, affect, motivation, cognitive difficulties, and waking dreams. The relationship between the self-report measures and performance in a visual search and memory task is also examined. Most of the psychological variables are significantly affected by the number of days of sleep deprivation, all are significantly affected by hour of day; but only sleepiness, affect and motivation are also significantly affected by the interaction between these variables. The peak hours for self-reported psychological complaints are generally between 0400 and 0800, while the lowest number of complaints are usually reported in the afternoon/early evening, between 1600 and 2000. In addition, the results showed that (a) the amplitude of the circadian component of the psychological data increased over the period of sleep loss, and (b) psychological data were more highly correlated with a measure of general performance than with accuracy. The mechanisms of sleep deprivation underlying its effects on psychological and performance measures are discussed.

A QoS-guaranteed multicast routing protocol

This paper considers the shaping of the amplitude spectra of perturbation signals for the identification of a thermostat system. The current approach in control engineering practice utilizes flat spectrum signals, which may not result in the highest possible accuracy. This research aims to investigate the effectiveness of optimal signals with amplitude spectra designed using two state-of-the-art software approaches, namely the model-based optimal signal excitation 2 (MOOSE2) design and the optimal excitation (optexcit) design, in improving estimation accuracy. Such a comparison on a real system is currently lacking. In particular, there exists a research gap on how the combined choice of signal and model structure affects performance measures. In this research, two model structures are used, which are the autoregressive with exogenous input (ARX) and the output error (OE) model structures. Four performance measures are compared, namely the determinant of the covariance matrix of the parameter estimates and the minimum error, mean error and maximum error in the frequency response. Results show that the optimal signals are effective in reducing the determinant of the covariance matrix and the maximum error in the frequency response for the thermostat system, when applied in combination with the ARX model structure. The flat spectrum signal remains very useful as a general broadband perturbation signal as it provides a good overall fit of the frequency response. The findings from this work highlight the benefits of applying optimal signals especially if the identification results are to be used for control, since these signals improve key performance measures which have direct implications on controller design.

Development of National Research and Clinical Agendas for Patient-Reported Outcomes in IR: Proceedings from a Multidisciplinary Consensus Panel

Performance measures such as alpha and the Sharpe ratio are typically based on sample returns net of fees. This implies the same weighing to sample returns and to fees. However, sample return parameters are noisy estimates of true parameters, while fees are known with certainty. Thus, intuition suggests that fees should be given more weight than sample returns. The authors formalize this intuition, and derive the optimal overweighing of fees. They show that the resulting generalized performance measures are better predictors of future net performance than the standard performance measures, and they better explain future fund flows.

Rating medical emergency teamwork performance: Development of the Team Emergency Assessment Measure (TEAM)

In this work the performance and computer time requirements of 15 classifiers are compared in images modeled by two-dimensional Gaussian Markov random fields which are represented by a causal autoregressive model of the second order. The per-pixel classifier and the object classifier directly or indirectly utilizing spectral-spatial characteristies of images are among them. The probability of misclassification (PMC) calculated analytically and experimentally on modeled data was used as a measure of a classifier performance. The influence of such factors as the object size and form, the inadequacy of a classifier and data models, the accuracy of spatial correlation estimation on the PMC is investigated. The following main results are obtained. The performance of object classifiers is much better than that of per-pixel classifiers. The PMC of object classifiers decreases rapidly with the increase of the size of an object. The performance of object classifiers indirectly incorporating spatial characteristics of an object (OCIND) and that of object classifiers directly incorporating spatial characteristics (OCDIR) is similar for the linear decision rule. The performance of OCDIR is much better than that of OCIND for the quadratic decision rule. Computationally, averaging object classifiers are fastest, next are OCIND and finally OCDIR. The cross-shaped object classifier is better and faster than the square-shaped object classifier for the same number of pixels.

Innovation, particularly in a dynamic environment, is crucial to a firm’s competitiveness. We test the hypothesis that managers’ performance, particularly their innovativeness, is related to the diversity of knowledge to which they are exposed in their interactions with colleagues. In a study of 106 managers in a high technology company, we find their performance benefits from increasing diversity of knowledge among their contacts. However, the effect of knowledge heterogeneity depends on the presence of sparse local networks. The joint influence of heterogeneity and sparse network structure is stronger for managerial innovation than for a more general measure of managerial performance. This suggests that the link between knowledge heterogeneity and performance relies on a process of knowledge synthesis involved in the generation of new ideas, with sparse networks affording managers the local autonomy needed for their development and implementation.

The problem-solving abilities of 4- and 5-year old children (N = 208) were assessed to test the hypothesis that high levels of compliance are negatively related to problem solving. Problem-solving competence was examined with a task-specific measure and a standardized measure of general problem-solving performance. Low compliant children performed better on both measures. The role of compliance in the cognitive development of girls and boys is discussed.