A novel coplanar waveguide (CPW)-fed dual-band dual-mode patch antenna for on-/off-body communications is proposed. The proposed antenna, which comprises a T-shaped patch with parasitic patches, a circular patch, and a CPW feed line, has dimensions of 30 mm × 45 mm × 3.2 mm. The proposed antenna operates in the 2.4 GHz ISM band (2.4 -2.485 GHz) for off-body communication, and in the 5.8 GHz ISM band (5.725 -5.875 GHz) for on-body communication. The antenna has a patch-like radiation pattern in the 2.4 GHz ISM band and a monopole-like radiation pattern in the 5.8 GHz ISM band. Index Terms-Co-planar waveguide, Patch antenna, Low-profile, Monopole-like, Wireless body area network (WBAN) 1536-1225 (c)
There has been a rapid increase in demand for premium and safe agricultural products. Protected systems, such as greenhouses, are being adopted to meet demand. Ease in environmental regulation required for optimal plant growth is one of the advantages of protected systems. However, drawbacks such as poor ventilation in greenhouses can be fatal to the human workforce. This has led to the development of robots for hazardous tasks. Considering mobile robots are required to navigate down every aisle to perform a task in a greenhouse, and it is difficult to predict at which point the robot will need to return to the start point, to offload or refill for transportation and spraying schedules, respectively or battery charges. It will be commercially constraining to manufacture robots for every greenhouse specification. Efficient navigation can be done through path planning or layout design. In this paper, the greenhouse layout optimization problem was formulated to find optimal points on each bed to create an access path that would enable a reduction in the total travel time from all points in the greenhouse to the base point. The optimization problem was solved using differential evolution (DE), an evolutionary algorithm. Furthermore, we considered: 1) required space for inter-bed and rotary robot navigation; 2) standard bed specification; 3) area of the greenhouse; and 4) base point for starting and terminating navigation. The applicability of the proposed method was demonstrated by carrying out the experimental simulations on several greenhouse sizes.
The growing importance of rice globally over the past three decades is evident in its strategic place in many countries’ food security planning policies. Still, its cultivation emits substantial greenhouse gases (GHGs). The Indica and Japonica sub-species of Oryza sativa L. are mainly grown, with Indica holding the largest market share. The awareness, economics, and acceptability of Japonica rice in a food-insecure Indica rice-consuming population were surveyed. The impact of parboiling on Japonica rice was studied and the factors which most impacted stickiness were investigated through sensory and statistical analyses. A comparison of the growing climate and greenhouse gas emissions of Japonica and Indica rice was carried out by reviewing previous studies. Survey results indicated that non-adhesiveness and pleasant aroma were the most preferred properties. Parboiling treatment altered Japonica rice’s physical and chemical properties, introducing gelatinization of starch and reducing adhesiveness while retaining micronutrient concentrations. Regions with high food insecurity and high consumption of Indica rice were found to have suitable climatic conditions for growing Japonica rice. Adopting the higher-yielding, nutritious Japonica rice whose cultivation emits less GHG in these regions could help strengthen food security while reducing GHGs in global rice cultivation.
Optimal placement of sensors in protected cultivation systems to maximize monitoring and control capabilities can guide effective decision-making toward achieving the highest levels of productivity and other desirable outcomes. Reinforcement learning, unlike conventional machine learning methods such as supervised learning does not require large, labeled datasets thereby providing opportunities for more efficient and unbiased design optimization. With the objective of determining the optimal locations of sensors in a greenhouse, a multi-arm bandit problem was formulated using the Beta distribution and solved by the Thompson sampling algorithm. A total of 56 two-in-one sensors designed to measure both internal air temperature and relative humidity were installed at a vertical distance of 1 m and a horizontal distance of 3m apart in a greenhouse used to cultivate strawberries. Data was collected over a period of seven months covering four major seasons, February (winter), March, April, and May (spring), June and July (summer), and October (autumn) and analyzed separately. Results showed unique patterns for sensor selection for temperature and relative humidity during the different months. Furthermore, temperature and relative humidity each had different optimal location selections suggesting that two-in-one sensors might not be ideal in these cases. The use of reinforcement learning to design optimal sensor placement in this study aided in identifying 10 optimal sensor locations for monitoring and controlling temperature and relative humidity.
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