Helmholtz resonators are often used to reduce noise. They are particularly useful when noise has a narrow frequency band. In this study we aim to broaden its narrow band characteristics by combining many resonators. Serial and parallel arrangements of resonators have been tested to obtain broader impedance mismatch characteristics in the broader band. Theoretical and experimental results explain these characteristics in the absence of mean flow. The serial arrangement mainly increases the peak of TL at the resonance frequency. But the parallel arrangement logarithmically increases the peak of TL and expands the bandwidth. The change of acoustic characteristics is explained by introducing an "equivalent impedance analysis." This shows that the transmission loss has a maximum value when the distance between resonators is / 4 of its wavelength. In this study we propose a novel design method that optimizes the arrangement of resonators while keeping the volume minimized. Various transmission loss characteristics are possible when we select different objective functions under constraints.
The significant role of plants can be observed through the dependency of animals and humans on them. Oxygen, materials, food and the beauty of the world are contributed by plants. Climate change, the decrease in pollinators, and plant diseases are causing significant decline in both quality and quantity of the plants and crops on global scale. In developed countries, above 80 percent of rural production is produced by sharecropping. However, due to widespread diseases in plants, yields are reported to have declined by more than a half. These diseases are identified and diagnosed by the agricultural and forestry department. Manual inspection on a large area of fields requires a huge amount of time and effort, thereby reduces the effectiveness significantly. To counter this problem, we propose an automatic disease detection and classification method in radish fields by using camera attached on Unmanned Aerial Vehicle (UAV) to capture high quality images of the fields and analyze them by extracting both color and texture features, then we perform segmentation on them using Kmeans clustering to select radish regions in the field and feeds them into convolutional neural network (CNN) to detect Fusarium wilt of radish efficiently at early stage and allow the authorities to take timely action which ensures the food safety for current and future generations.
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