Efficient Frontier and Benchmarking Models for Energy Multicast in Wireless Network Coding

This chapter introduces efficiency frontier and benchmarking concepts to evaluate the efficiency performance of wireless networks for multicast energy. These concepts are efficiency models based on the data envelopment analysis (DEA) technique. The DEA framework allows network administrators to evaluate the technical efficiency and determine how the inefficient wireless networks will attain a targeted efficiency frontier. In order to achieve efficiency frontier and benchmark by a wireless network, this chapter presents several models including the envelopment and the slack. The envelopment model evaluates the technical efficiency scores of each wireless network, while the slack model shows how the inefficient wireless network achieves efficiency frontier. The benchmark model evaluates the efficiency reference set and the lambda values of each network. The efficiency frontier algorithm has shown that many of the wireless networks sampled are inefficient. However, the algorithm has capability to help the inefficient wireless networks to achieve efficiency frontier and benchmark with their peers that are fully efficient

Performance modeling of virtual switching systems

International audienceVirtual switches are a key elements within the new paradigms of Software Defined Networking (SDN) and Network Function Virtualization (NFV). Unlike proprietary networking appliances, virtual switches come with a high level of flexibility in the management of their physical resources such as the number of CPU cores, their allocation to the switching function, and the capacities of the RX queues, which gives the opportunity for an efficient sizing of the system resources. We propose a model for the performance evaluation of a virtual switch. Our model resorts to servers with vacation to capture the involved interactions between queues resulting from the implemented polling strategies. The solution to the model is found using a simple fixed-point iteration and it provides estimates for customary performance metrics such as the attained throughput, the packet latency, the buffer occupancy and the packet loss rate. In the tens of explored examples, the predictions of the model were found to be accurate, thereby allowing their use for the purpose of sizing problems

Improvement of Vector Autoregression (VAR) estimation using Combine White Noise (CWN) technique

Previous studies revealed that Exponential Generalized Autoregressive Conditional Heteroscedastic (EGARCH) outperformed Vector Autoregression (VAR) when data exhibit heteroscedasticity. However, EGARCH estimation is not efficient when the data have leverage effect. Therefore, in this study the weaknesses of VAR and EGARCH were modelled using Combine White Noise (CWN). The CWN model was developed by integrating the white noise of VAR with EGARCH using Bayesian Model Averaging (BMA) for the improvement of VAR estimation. First, the standardized residuals of EGARCH errors (heteroscedastic variance) were decomposed into equal variances and defined as white noise series. Next, this series was transformed into CWN model through BMA. The CWN was validated using comparison study based on simulation and four countries real data sets of Gross Domestic Product (GDP). The data were simulated by incorporating three sample sizes with low, moderate and high values of leverages and skewness. The CWN model was compared with three existing models (VAR, EGARCH and Moving Average (MA)). Standard error, log-likelihood, information criteria and forecast error measures were used to evaluate the performance of the models. The simulation findings showed that CWN outperformed the three models when using sample size of 200 with high leverage and moderate skewness. Similar results were obtained for the real data sets where CWN outperformed the three models with high leverage and moderate skewness using France GDP. The CWN also outperformed the three models when using the other three countries GDP data sets. The CWN was the most accurate model of about 70 percent as compared with VAR, EGARCH and MA models. These simulated and real data findings indicate that CWN are more accurate and provide better alternative to model heteroscedastic data with leverage effect

Diabetic Reinopathy Classification using Deep Learning

With diabetes growing at an alarming rate, changes in the retina of diabetic patients causes a condition called diabetic retinopathy which eventually leads to blindness. Early detection of diabetic retinopathy is the best way to provide good timely treatment and thus prevent blindness. Many developed countries have put forward well-structured screening programs which screens every person diagnosed with diabetes at regular intervals. However, the cost of running these programs is increasing with ever increasing disease burden. These screening programs require well trained opticians or ophthalmologist which are expensive especially in developing countries. A global shortage of health care professionals is putting a pressing need to develop fast and efficient screening methods. Using artificial intelligent screening tools will help process and generate a plan for the patients thus skipping the health care provider needed to just classify the disease and will lower the burden on health care professional’s shortage significantly. A plethora of research exists to classify severity of diabetic retinopathy using traditional and end to end methods. In this thesis, we first trained and compared the performance of lightweight architecture MobileNetV2 with other classifiers like DenseNet121 and VGG16 using the Retinal fundus APTOS 2019 Kaggle dataset. We experimented with different image reprocessing techniques and employed various hyperparameter tuning techniques, and found the lightweight architecture MobileNetV2 to give better results in terms of AUC score which defines the ability of the classifier to separate between the classes. We then trained MobileNetV2 using handpicked custom dataset which was an amalgamation of 3 different publicly available datasets viz. the EyePacs Kaggle dataset, the APTOS 2019 Blindness detection dataset and the Messidor2 dataset. We enhanced the retinal features using bio-inspired retinal filters and tuned the hyper-parameters to achieve an accuracy of 91.68% and AUC score of 0.9 when tested on unseen data. The macro precision, recall, and f1-scores are 77.6%, 83.1%, and 80.1% respectively. Our results demonstrate that our computational efficient light weight model achieves promising results and can be deployed as a mobile application for clinical testing

Musculoskeletal Risks: RULA Bibliometric Review

The objective of this study was to reveal RULA method applications in terms of the knowledge, country, year and journal categories. The search was performed using the “Web of Science Core Collection”. The period from 1993 to April 2019 was selected. Eight hundred nine results were obtained, of which 226 were used. The largest number of publications was determined to be in the fields of industry and health and social assistance, which coincides with the OWAS and Standardized Nordic Questionnaire methods. By country, the USA stands out for its greater number of research studies and categories that are encompassed. By date, 2016 was the year when more studies were carried out, again coinciding with the Standardized Nordic Questionnaire. By journal, “Work—A Journal of Prevention Assessment and Rehabilitation” is highlighted, as it is for the REBA method as well. It was concluded that RULA can be applied to workers in different fields, usually in combination with other methods, while technological advancement provides benefits for its application

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2

Applicability of Neural Networks to Software Security

Software design flaws account for 50% software security vulnerability today. As attacks on vulnerable software continue to increase, the demand for secure software is also increasing thereby putting software developers under more pressure. This is especially true for those developers whose primary aim is to produce their software quickly under tight deadlines in order to release it into the market early. While there are many tools focusing on implementation problems during software development lifecycle (SDLC), this does not provide a complete solution in resolving software security problems. Therefore designing software with security in mind will go a long way in developing secure software. However, most of the current approaches used for evaluating software designs require the involvement of security experts because many software developers often lack the required expertise in making their software secure. In this research the current approaches used in integrating security at the design level is discussed and a new method of evaluating software design using neural network as evaluation tool is presented. With the aid of the proposed neural network tool, this research found out that software design scenarios can be matched to attack patterns that identify the security flaws in the design scenarios. Also, with the proposed neural network tool this research found out that the identified attack patterns can be matched to security patterns that can provide mitigation to the threat in the attack pattern

Flexibility Services to Minimize the Electricity Production from Fossil Fuels. A Case Study in a Mediterranean Small Island

The design of multi-carrier energy systems (MES) has become increasingly important in the last decades, due to the need to move towards more efficient, flexible and reliable power systems. In a MES, electricity, heating, cooling, water and other resources interact at various levels, in order to get optimized operation. The aim of this study is to identify the optimal combination of components, their optimal sizes and operating schedule allowing minimizing the annual cost for meeting the energy demand of Pantelleria, a Mediterranean island. Starting from the existing energy system (comprising diesel generators, desalination plant, freshwater storage, heat pumps and domestic hot water storages) the installation of solar resources (photovoltaic and solar thermal) and electrical storage were considered. In this way, the optimal scheduling of storage units injections, water desalination operation and domestic hot water production was deduced. An energy hub model was implemented using MATLAB to represent the problem. All equations in the model are linear functions, and variables are real or integer. Thus, a mixed integer linear programming algorithm was used for the solution of the optimization problem. Results prove that the method allows a strong reduction of operating costs of diesel generators also in the existing configuration

A genetic programming system with an epigenetic mechanism for traffic signal control

Traffic congestion is an increasing problem in most cities around the world. It impacts businesses as well as commuters, small cities and large ones in developing as well as developed economies. One approach to decrease urban traffic congestion is to optimize the traffic signal behaviour in order to be adaptive to changes in the traffic conditions. From the perspective of intelligent transportation systems, this optimization problem is called the traffic signal control problem and is considered a large combinatorial problem with high complexity and uncertainty. A novel approach to the traffic signal control problem is proposed in this thesis. The approach includes a new mechanism for Genetic Programming inspired by Epigenetics. Epigenetic mechanisms play an important role in biological processes such as phenotype differentiation, memory consolidation within generations and environmentally induced epigenetic modification of behaviour. These properties lead us to consider the implementation of epigenetic mechanisms as a way to improve the performance of Evolutionary Algorithms in solution to real-world problems with dynamic environmental changes, such as the traffic control signal problem. The epigenetic mechanism proposed was evaluated in four traffic scenarios with different properties and traffic conditions using two microscopic simulators. The results of these experiments indicate that Genetic Programming was able to generate competitive actuated traffic signal controllers for all the scenarios tested. Furthermore, the use of the epigenetic mechanism improved the performance of Genetic Programming in all the scenarios. The evolved controllers adapt to modifications in the traffic density and require less monitoring and less human interaction than other solutions because they dynamically adjust the signal behaviour depending on the local traffic conditions at each intersection