Design of a 3D Printed UWB Antenna for a Low-Cost Wireless Heart and Respiration Rate Monitoring

This paper introduces an ultra-wideband (UWB) horn antenna for a low-cost wireless heart and respiration rate monitoring that is manufactured by a 3D printing technology. The proposed design operates within 3.1-10.6 GHz. The horn antenna is designed within the 15-dB gain which is sufficient to be used in medical system. The horn was designed to calculate the gain in MATLAB using an approximation method and simulated using CST Microwave Studio. The proposed antenna is 4-6 GHz (G-Band) operating. A printed antenna which is supported by WR-187 rectangular waveguide is fabricated using low cost polylactic acid (PLA). The surface is then metalized using copper tape on the inside. The simulation result of reflection coefficient for different conductivity and thickness of coating metal is compared, the developed 3D printed antenna successfully operated within given frequency range of 3-11 GHz which covered the ultra-wideband frequency range

Sustainable bio-economy that delivers the environment-food-energy-water nexus objectives: the current status in Malaysia

Biomass is a promising resource in Malaysia for energy, fuels, and high value-added products. However, regards to biomass value chains, the numerous restrictions and challenges related to the economic and environmental features must be considered. The major concerns regarding the enlargement of biomass plantation is that it requires large amounts of land and environmental resources such as water and soil that arises the danger of creating severe damages to the ecosystem (e.g. deforestation, water pollution, soil depletion etc.). Regarded concerns can be diminished when all aspects associated with palm biomass conversion and utilization linked with environment, food, energy and water (EFEW) nexus to meet the standard requirement and to consider the potential impact on the nexus as a whole. Therefore, it is crucial to understand the detail interactions between all the components in the nexus once intended to look for the best solution to exploit the great potential of biomass. This paper offers an overview regarding the present potential biomass availability for energy production, technology readiness, feasibility study on the techno-economic analyses of the biomass utilization and the impact of this nexus on value chains. The agro-biomass resources potential and land suitability for different crops has been overviewed using satellite imageries and the outcomes of the nexus interactions should be incorporated in developmental policies on biomass. The paper finally discussed an insight of digitization of the agriculture industry as future strategy to modernize agriculture in Malaysia. Hence, this paper provides holistic overview of biomass competitiveness for sustainable bio-economy in Malaysia

IMAGE CLASSIFICATION FOR MAPPING OIL PALM DISTRIBUTION VIA SUPPORT VECTOR MACHINE USING SCIKIT-LEARN MODULE

The world has been alarmed with the global warming effects. Global warming has been a distress towards the environment, thus shorten the Earth’s lifespan. It is a challenging task to reduce the global warming effects in a short period, knowing that the human population is increasing along with the electricity and energy demand. In order to reduce the effects, renewable energy is presented as an alternative method to produce energy in a way that will not harm the environment. Oil palm is one of the agricultural crops that produces huge amount of biomass which can be processed and used as a renewable energy source. In 2016, Malaysia has reported over 5 million hectares of land were covered by oil palm plantations. Placing Malaysia as the second largest country of oil palm producer in the world has given it an advantage to produce renewable energy source. However, there is a need to monitor the sustainability of oil palm plantations in Malaysia via effective mapping approaches. This study utilised two different platforms (open source and commercial) using a machine learning algorithm namely Support Vector Machine (SVM) to perform oil palm mapping. An open source Python programming-based technique utilising Scikit-learn module was performed to map the oil palm distribution and the result produced had an overall accuracy of 91.39%. To support and validate the efficiency of the Python programming-based image classification, a commercial remote sensing software (ENVI) was used and compared by implementing the same SVM algorithm and the result showed an overall accuracy of 98.21%