Renewable energy development is crucial for resolving global warming issues and achieving

sustainable development goals. The objective of this paper is to reveal renewable energy trends

using statistical data to identify the most developed technologies in the field, their distribution by

territory, checking the interrelation between investments in R&D and patent activity.

The changes in total investments made in renewables are studied for 2004–2019 and the increasing

trend has been observed with some fluctuations in certain years, major current investments in solar

and wind energy are detected. The dynamics of international patenting activity in solar and wind

technologies, geothermal and fuel cells, the total number of PCT applications over the past 10 years

by geographic region are considered. The results show some differences across various renewable

technologies. Solar is the most patented, wind and fuel cells are less patented but solar and wind

have shown an uptrend for the last five years, while fuel cell technologies showed a decline after

2008. Geothermal is the least patented.

The comparison between investment and patent application trends in renewables undercovers some

important issues – the leading role of Japan in patenting under the PCT procedure, while China is

a major investor in renewable energy; the US and Europe with big investments in renewables are

also leading in patent registration.

Correlation between investment in R&D and patenting activity in the field of global renewable

energy should be emphasized (correlation coefficient R = 0,849 for 2002–2019). Since private

and public investment is strongly stimulated by domestic renewable energy policies, IP indicators

can help identify policy instruments and their effectiveness within the further research

framework.

Medicinal plants have a huge significance today as it is the root resource to treat several ailments and medical disorders that do not find a satisfactory cure using allopathy. The manual and physical identification of such plants requires experience and expertise and it can be a gradual and cumbersome task, in addition to resulting in inaccurate decisions. In an attempt to automate this decision making, a data set of leaves of 10 medicinal plant species were prepared and the Gray-level Co-occurence Matrix (GLCM) features were extracted. From our earlier implementations of the several machine learning algorithms, the k-nearest neighbor (KNN) algorithm was identified as best suited for classification using MATLAB 2019a and has been adopted here. Based on the confusion matrices for various k values, the optimum k was selected and the hardware implementation was implemented for the classifier on FPGA in this work. An accuracy of 88.3% was obtained for the classifier from the confusion chart. A custom intellectual property (IP) for the design is created and its verification is done on the ZedBoard for three classes of plants.

The report encompasses the activity of the Committee on Ethics in Science in the year 2016.