Owing to recent technological advancement, computers and other devices running several image editing applications can be further exploited for digital image processing operations. This paper evaluates various image processing techniques using matrix laboratory (MATLAB-based analytics). Compared to the conventional techniques, MATLAB gives several advantages for image processing. MATLAB-based technique provides easy debugging with extensive data analysis and visualization, easy implementation and algorithmic-testing without recompilation. Besides, MATLAB's computational codes can be enhanced and exploited to process and create simulations of both still and video images. Moreover, MATLAB codes are much concise compared to C++, thus making it easier for perusing and troubleshooting. MATLAB can handle errors prior to execution by proposing various ways to make the codes faster. The proposed technique enables advanced image processing operations such as image cropping/resizing, image denoising, blur removal, and image sharpening. The study aims at providing readers with the most recent MATLAB-based image processing application-tools. We also provide an empirical-based method using two-dimensional discrete cosine transform (2D-DCT) derived from its coefficients. Using the most recent algorithms running on MATLAB toolbox, we performed simulations to evaluate the performance of our proposed technique. The results largely present MATLAB as a veritable approach for image processing operations.

Synchrophasor technology is receiving a global acceptance for electric power grid Wide Area Measurement System, (WAMS), an important function in a smart power transmission grid. A critical challenge in the synchrophasor technology deployment is the optimal choice of Phasor Measurement Unit (PMU) locations on the power grid. Researchers have proposed several techniques and algorithms in this respect. This work evaluated some of the major techniques and established that the available techniques and the factors they considered are not sufficient for a real-life optimal PMU placement (OPP). It also pointed to a method that could be applied to achieve a practical and robust solution for effective PMU placement for synchrophasor applications in a real-life electric power grid. It, therefore, calls for a shift in paradigm in the studies for the optimal PMU placement locations.

This chapter presents the basic approach of microwave bandpass filter design for 5G network applications. The chapter serves as a reference source to microwave stakeholders with little or no filter design experience. It should help them to design and implement their first filter device using microstrip technology. A three-pole Chebyshev bandpass filter with centre frequency of 2.6 GHz, fractional bandwidth of 3%, passband ripple of 0.04321 dB, and return loss of 20 dB has been designed. The designed filter implementation is based on the Rogers RT/Duroid 6010LM substrate with a 10.7 dielectric constant and 1.27 mm thickness. The circuit model and microstrip layout results of the BPF are presented and show good agreement. The microstrip layout simulation results show that a less than 1.8 dB minimum insertion loss and a greater than 25 dB in-band return loss were achieved. The overall device size of the BPF is 18.0 mm by 10.7 mm, which is equivalent to 0.16λg x 0.09λg, where λg is the guided wavelength of the 50 Ohm microstrip line at the filter centre frequency.

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