ACARS:adaptive context - aware rate selection for DSRC in vehicular networks

Throughput plays a vital role for data transfer in Vehicular Networks which is useful for both safety and non-safety applications. An algorithm that adapts to mobile environment by using Context information has been proposed in this paper. Since one of the problems of existing rate adaptation algorithm is underutilization of link capacity in Vehicular environments, we have demonstrated that in wireless and mobile environments, vehicles can adapt to high mobility link condition and still perform better due to regular vehicles that will be out of communication range due to range checking and then de-congest the network thereby making the system perform better since fewer vehicles will contend for network resources. In this paper, we have design, implement and analyze ACARS, a more robust algorithm with significant increase in throughput performance and energy efficiency in the mist of high mobility of vehicles.

Implementing multicore real-time scheduling algorithms based on task splitting using ada 2012

Multiprocessors, particularly in the form of multicores, are becoming standard building blocks for executing reliable software. But their use for applications with hard real-time requirements is non-trivial. Well-known realtime scheduling algorithms in the uniprocessor context (Rate-Monotonic [1] or Earliest-Deadline-First [1]) do not perform well on multiprocessors. For this reason the scientific community in the area of real-time systems has produced new algorithms specifically for multiprocessors. In the meanwhile, a proposal [2] exists for extending the Ada language with new basic constructs which can be used for implementing new algorithms for real-time scheduling; the family of task splitting algorithms is one of them which was emphasized in the proposal [2]. Consequently, assessing whether existing task splitting multiprocessor scheduling algorithms can be implemented with these constructs is paramount. In this paper we present a list of state-of-art task-splitting multiprocessor scheduling algorithms and, for each of them, we present detailed Ada code that uses the new constructs.

Modelo de pérdida de paquetes para redes híbridas

El objetivo de este proyecto es la predicción de la pérdida de paquete. Para ello necesitaremos el modelado del canal. De esta manera, podremos determinar cuando una transmisión llega con éxito o no. En primer lugar, se han estudiado los algoritmos de adaptación de la tasa. Estos algoritmos mejoran el rendimiento de la comunicación. Por este motivo, el programa de simulación se basa en algunos de estos algoritmos. En paralelo, se han capturado medidas del canal terrestre para realizar el modelado. Finalmente, con un programa mucho más completo se ha simulado el comportamiento de una transmisión con el modelado del canal físico, y se han asumido algunas consideraciones, como las colisiones. Por lo tanto, se ha obtenido un resultado más realista, con el cual se ha analizado teóricamente las posibilidades de un enlace entre el canal terrestre y el canal satélite, para crear una red híbrida.

Aplicativo computacional da função discriminante quadrática para utilização em ciências experimentais

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Providing Reliable Data Transport for Dynamic Event Sensing in Wireless Sensor Networks

In this paper, we propose a Loss Tolerant Reliable (LTR) data transport mechanism for dynamic Event Sensing (LTRES) in WSNs. In LTRES, a reliable event sensing requirement at the transport layer is dynamically determined by the sink. A distributed source rate adaptation mechanism is designed, incorporating a loss rate based lightweight congestion control mechanism, to regulate the data traffic injected into the network so that the reliability requirement can be satisfied. An equation based fair rate control algorithm is used to improve the fairness among the LTRES flows sharing the congestion path. The performance evaluations show that LTRES can provide LTR data transport service for multiple events with short convergence time, low lost rate and high overall bandwidth utilization.

Cardiopulmonary performance testing using a robotics-assisted tilt table: Feasibility assessment in able-bodied subjects

BACKGROUND: Robotics-assisted tilt table technology was introduced for early rehabilitation of neurological patients. It provides cyclical stepping movement and physiological loading of the legs. The aim of the present study was to assess the feasibility of this type of device for peak cardiopulmonary performance testing using able-bodied subjects. METHODS: A robotics-assisted tilt table was augmented with force sensors in the thigh cuffs and a work rate estimation algorithm. A custom visual feedback system was employed to guide the subjects' work rate and to provide real time feedback of actual work rate. Feasibility assessment focused on: (i) implementation (technical feasibility), and (ii) responsiveness (was there a measurable, high-level cardiopulmonary reaction?). For responsiveness testing, each subject carried out an incremental exercise test to the limit of functional capacity with a work rate increment of 5 W/min in female subjects and 8 W/min in males. RESULTS: 11 able-bodied subjects were included (9 male, 2 female; age 29.6 ± 7.1 years: mean ± SD). Resting oxygen uptake (O_{2}) was 4.6 ± 0.7 mL/min/kg and O_{2}peak was 32.4 ± 5.1 mL/min/kg; this mean O_{2}peak was 81.1% of the predicted peak value for cycle ergometry. Peak heart rate (HRpeak) was 177.5 ± 9.7 beats/min; all subjects reached at least 85% of their predicted HRpeak value. Respiratory exchange ratio (RER) at O_{2}peak was 1.02 ± 0.07. Peak work rate) was 61.3 ± 15.1 W. All subjects reported a Borg CR10 value for exertion and leg fatigue of 7 or more. CONCLUSIONS: The robotics-assisted tilt table is deemed feasible for peak cardiopulmonary performance testing: the approach was found to be technically implementable and substantial cardiopulmonary responses were observed. Further testing in neurologically-impaired subjects is warranted.

Feasibility of cardiopulmonary exercise testing and training using a robotics-assisted tilt table in dependent-ambulatory stroke patients

BACKGROUND: We evaluated the feasibility of an augmented robotics-assisted tilt table (RATT) for incremental cardiopulmonary exercise testing (CPET) and exercise training in dependent-ambulatory stroke patients. METHODS: Stroke patients (Functional Ambulation Category ≤ 3) underwent familiarization, an incremental exercise test (IET) and a constant load test (CLT) on separate days. A RATT equipped with force sensors in the thigh cuffs, a work rate estimation algorithm and real-time visual feedback to guide the exercise work rate was used. Feasibility assessment considered technical feasibility, patient tolerability, and cardiopulmonary responsiveness. RESULTS: Eight patients (4 female) aged 58.3 ± 9.2 years (mean ± SD) were recruited and all completed the study. For IETs, peak oxygen uptake (V'O2peak), peak heart rate (HRpeak) and peak work rate (WRpeak) were 11.9 ± 4.0 ml/kg/min (45 % of predicted V'O2max), 117 ± 32 beats/min (72 % of predicted HRmax) and 22.5 ± 13.0 W, respectively. Peak ratings of perceived exertion (RPE) were on the range "hard" to "very hard". All 8 patients reached their limit of functional capacity in terms of either their cardiopulmonary or neuromuscular performance. A ventilatory threshold (VT) was identified in 7 patients and a respiratory compensation point (RCP) in 6 patients: mean V'O2 at VT and RCP was 8.9 and 10.7 ml/kg/min, respectively, which represent 75 % (VT) and 85 % (RCP) of mean V'O2peak. Incremental CPET provided sufficient information to satisfy the responsiveness criteria and identification of key outcomes in all 8 patients. For CLTs, mean steady-state V'O2 was 6.9 ml/kg/min (49 % of V'O2 reserve), mean HR was 90 beats/min (56 % of HRmax), RPEs were > 2, and all patients maintained the active work rate for 10 min: these values meet recommended intensity levels for bouts of training. CONCLUSIONS: The augmented RATT is deemed feasible for incremental cardiopulmonary exercise testing and exercise training in dependent-ambulatory stroke patients: the approach was found to be technically implementable, acceptable to the patients, and it showed substantial cardiopulmonary responsiveness. This work has clinical implications for patients with severe disability who otherwise are not able to be tested.

Content-Aware Delivery of Scalable Video in Network Coding Enabled Named Data Networks

In this work, we propose a novel network coding enabled NDN architecture for the delivery of scalable video. Our scheme utilizes network coding in order to address the problem that arises in the original NDN protocol, where optimal use of the bandwidth and caching resources necessitates the coordination of the forwarding decisions. To optimize the performance of the proposed network coding based NDN protocol and render it appropriate for transmission of scalable video, we devise a novel rate allocation algorithm that decides on the optimal rates of Interest messages sent by clients and intermediate nodes. This algorithm guarantees that the achieved flow of Data objects will maximize the average quality of the video delivered to the client population. To support the handling of Interest messages and Data objects when intermediate nodes perform network coding, we modify the standard NDN protocol and introduce the use of Bloom filters, which store efficiently additional information about the Interest messages and Data objects. The proposed architecture is evaluated for transmission of scalable video over PlanetLab topologies. The evaluation shows that the proposed scheme performs very close to the optimal performance

Investigation of robotics-assisted tilt table technology for cardiopulmonary exercise testing in stroke patients

Due to the lack of exercise testing devices that can be employed in stroke patients with severe disability, the aim of this PhD research was to investigate the clinical feasibility of using a robotics-assisted tilt table (RATT) as a method for cardiopulmonary exercise testing (CPET) and exercise training in stroke patients. For this purpose, the RATT was augmented with force sensors, a visual feedback system and a work rate calculation algorithm. As the RATT had not been used previously for CPET, the first phase of this project focused on a feasibility study in 11 healthy able-bodied subjects. The results demonstrated substantial cardiopulmonary responses, no complications were found, and the method was deemed feasible. The second phase was to analyse validity and test-retest reliability of the primary CPET parameters obtained from the RATT in 18 healthy able-bodied subjects and to compare the outcomes to those obtained from standard exercise testing devices (a cycle ergometer and a treadmill). The results demonstrated that peak oxygen uptake (V'O2peak) and oxygen uptake at the submaximal exercise thresholds on the RATT were ̴20% lower than for the cycle ergometer and ̴30% lower than on the treadmill. A very high correlation was found between the RATT vs the cycle ergometer V'O2peak and the RATT vs the treadmill V'O2peak. Test-retest reliability of CPET parameters obtained from the RATT were similarly high to those for standard exercise testing devices. These findings suggested that the RATT is a valid and reliable device for CPET and that it has potential to be used in severely impaired patients. Thus, the third phase was to investigate using the RATT for CPET and exercise training in 8 severely disabled stroke patients. The method was technically implementable, well tolerated by the patients, and substantial cardiopulmonary responses were observed. Additionally, all patients could exercise at the recommended training intensity for 10 min bouts. Finally, an investigation of test-retest reliability and four-week changes in cardiopulmonary fitness was carried out in 17 stroke patients with various degrees of disability. Good to excellent test-retest reliability and repeatability were found for the main CPET variables. There was no significant difference in most CPET parameters over four weeks. In conclusion, based on the demonstrated validity, reliability and repeatability, the RATT was found to be a feasible and appropriate alternative exercise testing and training device for patients who have limitations for use of standard devices.

Quality-driven resource utilization methods for video streaming in wireless communication networks

This research is focused on the optimisation of resource utilisation in wireless mobile networks with the consideration of the users’ experienced quality of video streaming services. The study specifically considers the new generation of mobile communication networks, i.e. 4G-LTE, as the main research context. The background study provides an overview of the main properties of the relevant technologies investigated. These include video streaming protocols and networks, video service quality assessment methods, the infrastructure and related functionalities of LTE, and resource allocation algorithms in mobile communication systems. A mathematical model based on an objective and no-reference quality assessment metric for video streaming, namely Pause Intensity, is developed in this work for the evaluation of the continuity of streaming services. The analytical model is verified by extensive simulation and subjective testing on the joint impairment effects of the pause duration and pause frequency. Various types of the video contents and different levels of the impairments have been used in the process of validation tests. It has been shown that Pause Intensity is closely correlated with the subjective quality measurement in terms of the Mean Opinion Score and this correlation property is content independent. Based on the Pause Intensity metric, an optimised resource allocation approach is proposed for the given user requirements, communication system specifications and network performances. This approach concerns both system efficiency and fairness when establishing appropriate resource allocation algorithms, together with the consideration of the correlation between the required and allocated data rates per user. Pause Intensity plays a key role here, representing the required level of Quality of Experience (QoE) to ensure the best balance between system efficiency and fairness. The 3GPP Long Term Evolution (LTE) system is used as the main application environment where the proposed research framework is examined and the results are compared with existing scheduling methods on the achievable fairness, efficiency and correlation. Adaptive video streaming technologies are also investigated and combined with our initiatives on determining the distribution of QoE performance across the network. The resulting scheduling process is controlled through the prioritization of users by considering their perceived quality for the services received. Meanwhile, a trade-off between fairness and efficiency is maintained through an online adjustment of the scheduler’s parameters. Furthermore, Pause Intensity is applied to act as a regulator to realise the rate adaptation function during the end user’s playback of the adaptive streaming service. The adaptive rates under various channel conditions and the shape of the QoE distribution amongst the users for different scheduling policies have been demonstrated in the context of LTE. Finally, the work for interworking between mobile communication system at the macro-cell level and the different deployments of WiFi technologies throughout the macro-cell is presented. A QoEdriven approach is proposed to analyse the offloading mechanism of the user’s data (e.g. video traffic) while the new rate distribution algorithm reshapes the network capacity across the macrocell. The scheduling policy derived is used to regulate the performance of the resource allocation across the fair-efficient spectrum. The associated offloading mechanism can properly control the number of the users within the coverages of the macro-cell base station and each of the WiFi access points involved. The performance of the non-seamless and user-controlled mobile traffic offloading (through the mobile WiFi devices) has been evaluated and compared with that of the standard operator-controlled WiFi hotspots.

Optimising QoE distribution for video applications through LTE-WiFi interworking

Mobile WiFi devices are becoming increasingly popular in non-seamless and user-controlled mobile traffic offloading alongside the standard WiFi hotspots. Unlike the operator-controlled hotspots, a mobile WiFi device relies on the capacity of the macro-cell for the data rate allocated to it. This type of devices can help offloading data traffic from the macro-cell base station and serve the end users within a closer range, but will change the pattern of resource distributions operated by the base station. We propose a resource allocation scheme that aims to optimize user quality of experience (QoE) when accessing video services in the environment where traffic offloading is taking place through interworking between a mobile communication system and low range wireless LANs. In this scheme, a rate redistribution algorithm is derived to perform scheduling which is controlled by a no-reference quality assessment metric in order to achieve the desired trade-offs between efficiency and fairness. We show the performance of this algorithm in terms of the distribution of the allocated data rates throughout the macro-cell investigated and the service coverage offered by the WiFi access point.

Enhanced connectivity and QoE through LTE-WiFi interworking in Smart Cities

Mobile communication and networking infrastructures play an important role in the development of smart cities, to support real-time information exchange and management required in modern urbanization. Mobile WiFi devices that help offloading data traffic from the macro-cell base station and serve the end users within a closer range can significantly improve the connectivity of wireless communications between essential components including infrastructural and human devices in a city. However, this offloading function through interworking between LTE and WiFi systems will change the pattern of resource distributions operated by the base station. In this paper, a resource allocation scheme is proposed to ensure stable service coverage and end-user quality of experience (QoE) when offloading takes place in a macro-cell environment. In this scheme, a rate redistribution algorithm is derived to form a parametric scheduler to meet the required levels of efficiency and fairness, guided by a no-reference quality assessment metric. We show that the performance of resource allocation can be regulated by this scheduler without affecting the service coverage offered by the WLAN access point. The performances of different interworking scenarios and macro-cell scheduling policies are also compared.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.

Squared Extrapolation Methods (SQUAREM): A New Class of Simple and Efficient Numerical Schemes for Accelerating the Convergence of the EM Algorithm

We derive a new class of iterative schemes for accelerating the convergence of the EM algorithm, by exploiting the connection between fixed point iterations and extrapolation methods. First, we present a general formulation of one-step iterative schemes, which are obtained by cycling with the extrapolation methods. We, then square the one-step schemes to obtain the new class of methods, which we call SQUAREM. Squaring a one-step iterative scheme is simply applying it twice within each cycle of the extrapolation method. Here we focus on the first order or rank-one extrapolation methods for two reasons, (1) simplicity, and (2) computational efficiency. In particular, we study two first order extrapolation methods, the reduced rank extrapolation (RRE1) and minimal polynomial extrapolation (MPE1). The convergence of the new schemes, both one-step and squared, is non-monotonic with respect to the residual norm. The first order one-step and SQUAREM schemes are linearly convergent, like the EM algorithm but they have a faster rate of convergence. We demonstrate, through five different examples, the effectiveness of the first order SQUAREM schemes, SqRRE1 and SqMPE1, in accelerating the EM algorithm. The SQUAREM schemes are also shown to be vastly superior to their one-step counterparts, RRE1 and MPE1, in terms of computational efficiency. The proposed extrapolation schemes can fail due to the numerical problems of stagnation and near breakdown. We have developed a new hybrid iterative scheme that combines the RRE1 and MPE1 schemes in such a manner that it overcomes both stagnation and near breakdown. The squared first order hybrid scheme, SqHyb1, emerges as the iterative scheme of choice based on our numerical experiments. It combines the fast convergence of the SqMPE1, while avoiding near breakdowns, with the stability of SqRRE1, while avoiding stagnations. The SQUAREM methods can be incorporated very easily into an existing EM algorithm. They only require the basic EM step for their implementation and do not require any other auxiliary quantities such as the complete data log likelihood, and its gradient or hessian. They are an attractive option in problems with a very large number of parameters, and in problems where the statistical model is complex, the EM algorithm is slow and each EM step is computationally demanding.

SLBN: A Scalable Max-min Fair Algorithm for Rate-Based Explicit Congestion Control

The growth of the Internet has increased the need for scalable congestion control mechanisms in high speed networks. In this context, we propose a rate-based explicit congestion control mechanism with which the sources are provided with the rate at which they can transmit. These rates are computed with a distributed max-min fair algorithm, SLBN. The novelty of SLBN is that it combines two interesting features not simultaneously present in existing proposals: scalability and fast convergence to the max-min fair rates, even under high session churn. SLBN is scalable because routers only maintain a constant amount of state information (only three integer variables per link) and only incur a constant amount of computation per protocol packet, independently of the number of sessions that cross the router. Additionally, SLBN does not require processing any data packet, and it converges independently of sessions' RTT. Finally, by design, the protocol is conservative when assigning rates, even in the presence of high churn, which helps preventing link overshoots in transient periods. We claim that, with all these features, our mechanism is a good candidate to be used in real deployments.