Understanding Traffic Characteristics in a Server to Server Data Center Network

The number of Data Centers and the servers present in them has been on the rise over the last decade with the advent of cloud computing, social networking, Big data analytics etc. This has eventually led to the increase in the power consumption of the Data Center due to the power hungry interconnection fabric which consists of switches and routers. The scalability of the data center has also become a problem due to the interconnect cabling complexity which is also responsible for the increase in the energy used for cooling the data center as these bundles of wires reduce the air flow in the data center. The maintenance costs of the data center is high due to this reason. This brings the challenge of reducing the power consumption as well as improving the scalability of the data center. There is a lot of cost involved in the establishment of a network in a data center and this network is one of the main source of power consumption. Therefore, there is a need to accurately characterize the data center network before its construction which requires the simulation of the data center models. For the simulation of data center models, we require the traffic which is identical to that of an actual data center so that the results will be similar to a real time data center. Traditional data center networks have a wired communication fabric, which is not scalable and contributes largely to the power consumption. This has led to the investigation of other methods. There have been transceivers designed that can support the unlicensed 60 GHz spectrum, supporting high bandwidth similar to the wired network present in traditional data centers. These wireless links have spatial reusability and the data centers can make use of this communication medium to meet the high bandwidth demands and also reduce the use of cable thereby bringing down the cost and the power consumption. This thesis studies the previous traffic models used in the simulation of a data center network. Traffic collected from ten different data centers is then characterized and modelled based on various probability distributions. The implementation of the model tries to generate traffic similar to that of an actual data center. The Data Center Network is then simulated using the traffic generated and the performance of the wired data center is quantified in terms of metrics like throughput, latency and the power consumption of the data center networks

Engineering Curriculum Design - Understanding motivational variables and their influence on self-directed learners when using 1:1 mobile devices.

Engineering curriculum design and delivery within the framework of budget restraints, learning outcome policies and industry standards, is a complex task that understandably universities and the engineering industry invest significant resources. It would be expected that what is actually occurring within the engineering learning space is a reflection of the constraints upon the industry, producing graduates, and products and services that provide a return on investment through intellectual capital. Firstly, the literature review will contextualise and explain the engineering student’s motivational variables to actively engage in their learning spaces, and how this may be applied by curriculum designers to improve the quality and delivery of courses. In particular, what are the intrinsic and extrinsic motivational variables and associated values that student’s desire during their engineering learning experience. Secondly, the research study will explore how motivational theory can be applied to the stages of ‘active learning’ when integrating 1:1 mobile devices for engineering learning. 1:1 mobile devices include iPad, mobile phones, Surface Tablets or handheld Wi-Fi or Internet accessible device used for learning purposes. It is not fully understood how to influence ‘active learning using existing teaching and learning strategies. How to influence an engineering undergraduate student to prioritise the use of 1:1 mobile devices as a means to source prescribed and unprescribed curriculum resources to improve learning outcomes. Is it unreasonable to expect engineering students to be constrained to the learning resources supplied by the engineering course facilitated, or should engineering students be encouraged to use their own initiative and find their own supporting information

Engineering fundamentals in a new undergraduate curriculum

CONTEXT In recent years there has been a push in Engineering education to change the basic model fromstudents learning discrete subjects, followed by design projects in third and fourth year, to learningand practicing the design process from the first year. At the same time, there has also been a pushtowards “active learning” (Prince, 2004) as opposed to the more traditional lecture/tutorial/practicalapproach. This year, Deakin University has launched a new design-centred curriculum inundergraduate engineering. Named “Project-Oriented Design-Based Learning” (PODBL), the newcourse structure is running in first and second years. In semester one of first year in the new course,students enrol in one double-unit of design, one unit of maths, and one unit of fundamental science.PURPOSE This work seeks to determine whether a new fundamental-science unit called “EngineeringFundamentals” fulfils the educational needs of first-year students in the PODBL curriculum. It alsoseeks to determine student perceptions of the new unit.APPROACH The unit was first offered in semester-one, 2016 to two separate on-campus cohorts and an offcampuscohort. Innovations in this unit include using the CADET model for teaching combinedpractical-tutorial seminars, a shift in lectures from delivering conceptual content to teaching problemsolving and applications (flipping the classroom), and extensive use of online videos and study guidesfor delivering primary content (Cloud Learning). Student learning was assessed by means of problembasedonline quizzes, practical reports, and a final exam. Student perceptions were queried by astandard unit-evaluation system and by a more focussed set of surveys given to students in threeseparate cohorts.RESULTS The academic results in this unit were compared with those in the previous unit. No substantialdifferences were observed in the marks of this unit in 2016 compared with the 2015 marks of thecorresponding previous physics unit. On-campus students showed more general satisfaction with theunit than did off-campus students. However, not all on-campus students were happy with the flippedclassroommodel.CONCLUSIONS As the course changes from a traditional approach to a design and project-based approach, it is best ifall units in the course adapt in some way to the new teaching style. Not all units need be completelyproject or design based. In the case of “Engineering Fundamentals,” we believe that due to the widevariety of topics covered, making the entire unit design-based is inappropriate. However, some designand project components can be built into the unit via the practicals. Semester one 2016 was asuccessful first offering of the unit. We recommend that in future years a design/project component beconsidered for the unit’s practicals

Contrast echocardiography: a practical guideline from the British Society of Echocardiography

Ultrasound contrast agents (UCAs) have a well-established role in clinical cardiology. Contrast echocardiography has evolved into a routine technique through the establishment of contrast protocols, an excellent safety profile, and clinical guidelines which highlight the incremental prognostic utility of contrast enhanced echocardiography. This document aims to provide practical guidance on the safe and effective use of contrast; reviews the role of individual staff groups; and training requirements to facilitate its routine use in the echocardiography laboratory

2dCNN-BiCuDNNLSTM: Hybrid Deep-Learning-Based Approach for Classification of COVID-19 X-ray Images

The coronavirus (COVID-19) is a major global disaster of humankind, in the 21st century. COVID-19 initiates breathing infection, including pneumonia, common cold, sneezing, and coughing. Initial detection becomes crucial, to classify the virus and limit its spread. COVID-19 infection is similar to other types of pneumonia, and it may result in severe pneumonia, with bundles of illness onsets. This research is focused on identifying people affected by COVID-19 at a very early stage, through chest X-ray images. Chest X-ray classification is a beneficial method in the identification, follow up, and evaluation of treatment efficiency, for people with pneumonia. This research, also, considered chest X-ray classification as a basic method to evaluate the existence of lung irregularities in symptomatic patients, alleged for COVID-19 disease. The aim of this research is to classify COVID-19 samples from normal chest X-ray images and pneumonia-affected chest X-ray images of people, for early identification of the disease. This research will help people in diagnosing individuals for viruses and insisting that people receive proper treatment as well as preventive action, to stop the spread of the virus. To provide accurate classification of disease in patients’ chest X-ray images, this research proposed a novel classification model, named 2dCNN-BiCuDNNLSTM, which combines two-dimensional Convolutional Neural Network (CNN) and a Bidirectional CUDA Deep Neural Network Long Short-Term Memory (BiCuDNNLSTM). Deep learning is known for identifying the patterns in available data that will be helpful in accurate classification of disease. The proposed model (2dCNN and BiCuDNNLSTM layers, with proper hyperparameters) can differentiate normal chest X-rays from viral pneumonia and COVID-19 ones, with high accuracy. A total of 6863 X-ray images (JPEG) (1000 COVID-19 patients, 3863 normal cases, and 2000 pneumonia patients) have been engaged, to examine the achievement of the suggested neural network; 80% of the images dataset for every group is received for proposed model training, 10% is accepted for validation, and 10% is accepted for testing. It is observed that the proposed model acquires the towering classification accuracy of 93%. The proposed network is used for predictive analysis, to prompt people regarding the risk of early detection of COVID-19. X-ray images help to classify people with COVID-19 variants and to indicate the severity of disease in the future. This study demonstrates the effectiveness of the proposed CUDA-enabled hybrid deep learning models, to classify the X-ray image data, with a high accuracy of detecting COVID-19. It reveals that the proposed model can be applicable in numerous virus classifications. The chest X-ray classification is a commonly available and reasonable approach, for diagnosing people with lower respiratory signs or suspected COVID-19. Therefore, it is demonstrated that the proposed model has an efficient and promising accomplishment for classifying COVID-19 through X-ray images. The proposed hybrid model can, efficiently, preserve the comprehensive characteristic facts of the image data, for more exceptional concluding classification results than an individual neural network

Evaluating assessment practices in design based learning environment

This investigation is focused on evaluating assessment practices in design based learning environment. The School of Engineering at Deakin University practices project/design based learning as one of its learning and teaching approach. When identifying graduate attributes particularly for undergraduate engineering programs in Australia, the program accrediting body Engineers Australia (EA) initiates a set of graduate attribute elements which was mentioned in “Stage1 competencies and elements of competency”. Stage1 competencies state that one of the important engineering application ability for graduates is ‘application of systematic engineering synthesis and design processes’. By practicing the design focused learning environment and evaluating students perceptions, This investigation examines students’ experiences of assessment practices in their curriculum through an online survey given to the same cohort of students in third year and fourth year undergraduate engineering

Ethnoveterinary medicine of the Shervaroy Hills of Eastern Ghats, India as alternative medicine for animals

The Eastern Ghats of India is well known for its wealth of natural vegetation and Shervaroy is a major hill range of the Eastern Ghats of Tamil Nadu. Ethnomedicinal studies in the Eastern Ghats of Tamil Nadu or the Shervaroy Hills have been carried out by various researchers. However, there is not much information available on ethnoveterinary medicine in the Eastern Ghats of India. The aim of this study was to examine the potential use of folk plants as alternative medicine for cattle to cure various diseases in the Shervaroy Hills of the Eastern Ghats. Based on interactions with traditional medicine practitioners, it has been observed that a total of 21 medicinal plants belonging to 16 families are used to cure various diseases such as mastitis, enteritis, arthritis, stomatitis, salivation from the mouth, wounding, and conjunctivitis in animals. It has been observed that the traditional knowledge of ethnoveterinary medicine is now confined only among the surviving older people and a few practitioners in the tribal communities of the Shervaroy Hills. Unfortunately, no serious attempts have been made to document and preserve this immense treasure of traditional knowledge