BSc in Electrical and Electronics Engineering

Background: Diabetic peripheral neuropathy (DSPN), a major form of diabetic neuropathy, is a complication that arises in long-term diabetic patients. Even though the application of machine learning (ML) in disease diagnosis is a very common and well-established field of research, its application in diabetic peripheral neuropathy (DSPN) diagnosis using composite scoring techniques like Michigan Neuropathy Screening Instrumentation (MNSI), is very limited in the existing literature. Method: In this study, the MNSI data were collected from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials. Two different datasets with different MNSI variable combinations based on the results from the eXtreme Gradient Boosting feature ranking technique were used to analyze the performance of eight different conventional ML algorithms. Results: The random forest (RF) classifier outperformed other ML models for both datasets. However, all ML models showed almost perfect re...

A force-invariant feature extraction method derives identical information for all force levels. However, the physiology of muscles makes it hard to extract this unique information. In this context, we propose an improved force-invariant feature extraction method based on nonlinear transformation of the power spectral moments, changes in amplitude, and the signal amplitude along with spatial correlation coefficients between channels. Nonlinear transformation balances the forces and increases the margin among the gestures. Additionally, the correlation coefficient between channels evaluates the amount of spatial correlation; however, it does not evaluate the strength of the electromyogram signal. To evaluate the robustness of the proposed method, we use the electromyogram dataset containing nine transradial amputees. In this study, the performance is evaluated using three classifiers with six existing feature extraction methods. The proposed feature extraction method yields a higher p...

A solar panel receives the maximum sunlight when it is perpendicular to the sun’s rays, but the sunlight direction changes regularly with changing time. Fixed solar panel has a fixed orientation to the sky and does not turn to follow the sun. So, the fixed solar panel cannot able to generate optimum energy. The proposed system solves the problem by an arrangement for the solar panel to track the sun by rotating the solar panel according to the sun rays direction with respect to time and to generate the maximum power. Moreover, LED based street lights with auto intensity control using solar power from photovoltaic cells can be more efficient and cost effective for the users.
In this project, a solar panel is coupled to a servo gear motor to track the sun, so that maximum sun light can incident upon the panel at any given time of the day. Photovoltaic panels are used for charging batteries by converting the sunlight into electricity. A charge controller circuit is used to control the charging. For accomplishing this part, simulation was done by Proteus. In addition, the intensity of street lights will be controlled automatically by this generated power. The intensity will be kept high during the peak hours (darkness is more) and the intensity can be reduced progressively till morning to save the power. Microcontroller is used to control the whole process of the proposed system. This project presents the analysis of street light with auto tracking system. By using this auto tracking system the conversion efficiency can be increased of the solar power generation. It is commercially beneficial, easy to manage, consumed low energy, cost effective and world-wide operational. Finally it gives a combined knowledge about electronics and renewable energy.