We can not imagine our daily lives without computers. Computer networks have become such an inseparable part of our personal and professional domains that even an hour-long outage can cause organizations to cost millions of dollars. A network engineer is responsible for designing, implementing, and monitoring the local and wide area networks of an organization.
A network engineer designs the system configurations, documents, and manages the installation of a new network. They also maintain and upgrade existing systems as, and when necessary. In addition to this, network engineers also ensure that network firewalls, and security is up to date. With the advent of data-driven and cloud-driven approaches, the role of a network engineer is very dynamic and ever-evolving.
As a student of network engineering fundamentals, you will learn the skills of programming, network, and web security. You are also introduced to the role of entrepreneurship in information technology, and network design. The basic and advanced concepts of wireless networking engineering and project management are also part of this course. If you are stuck in any of these topics and their assessment components, reach out to avail of our Network engineering fundamentals assignment help. With a team of industry-based experts and people certified in the relevant skills, you’ll receive professional mentoring for your assignment solution on Network engineering fundamentals.
A degree in Bachelor of Network Engineering or Bachelor of Computer networking and Telecommunications course is a globally recognized qualification for building a future career in multiple domains of Software industries. A few of such roles where network engineering students can be imbibed are given below:
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Due to the ever-increasing demand for network engineers and career-ready skilled professionals, Network engineering fundamentals and similar courses are offered by a lot of reputed universities worldwide. A list of such universities in Australia is given below for your ready reference:
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Computer vision is a multidisciplinary field that cannot be separated with image processing techniques and Neuro-Computing specifically Deep Learning (DL) algorithms, in recent times DL techniques enable computer vision to understand the content of an image, moreover, it is working hand in hand with image processing techniques because image preprocessing are essential components in digital image analysis. Therefore, the remarkable advancement recorded by computer vision today such as in remote sensing, security, medical imaging, and robotics, etc. According to a study by Ibrahim Goni et al. (2021), various image Preprocessing software for deep learning algorithms are now available for the implementation of computer vision research but the common ones are MatconvNet (MATLAB), Tensorflow (C++ and python), R, Caffe (C++), Torch (C and Lua), Keras (python), Deeplearning4j (Java), MxNet, Theano (Python), Gluon (C++), OpeenDeep (Python), NTK (C++) and ConvnetJs. Also, several issues in the area of DL concerning image processing are still in silent mode and need to be explored by future network engineers.
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Ibrahim Goni, Asabe Sandra Ahmadu, Yusuf Musa Malgwi, Image Processing Techniques and Neuro-computing Algorithms in Computer Vision, Advances in Networks. Vol. 9, No. 2, 2021, pp. 33-38. DOI: 10.11648/j.net.20210902.12
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