Abstract. This paper presents the analysis of dielectric properties of agricultural waste for microwave communication application such as microwave absorber and antenna. The residues products -rice straw, rice husk, banana leaves and sugar cane bagasse were studied in the range between 1-20GHz. Firstly, the 2 types of resins namely Epoxy der 331 and Polyamine clear hardener were mixed with the agricultural waste materials to produce the small size of agricultural waste sample. Then, the sample were measured using PNA network analyzer. The permittivity and tangent loss of different agricultural waste samples have been measured using dielectric probe technique. Besides, other objectives of this paper is to replace the conventional printed circuit board (PCB) using FR4, Taconic, and Roger material with the agricultural waste material. Besides that, the different percentage of filer for each agricultural waste materials were also investigated to specify the best material to be used as the substrate board and as the resonant material. the result shows the average of dielectric constants and the average of the tangent loss of agricultural waste materials.
In multiprocessor systems, dynamic cache distribution has been used to increase system performance by effectively partitioning the cache resources. However, different performance metrics used at runtime used to dynamically decide the partition sizes can give different impacts on performance, as well as varying impacts on the hardware cost of the system. In this paper, we propose an Adaptive CPI-based Cache Partitioning (ACCP) scheme to provide better utilisation of the shared cache resources among the competing applications in the system. ACCP uses performance gain estimations of the cache, without incurring significant hardware overhead. It aims to allow all applications in the system to run at approximately the same speed by accelerating the slowest application without significantly decelerating the others. We evaluated the ACCP on a quad-core system on which it achieved on average 23% reduction in miss rate, compared to an unpartitioned shared cache. ACCP also yields a similar IPC throughput improvement to a well-known UCP scheme, and better performance compared to the CPI by Muralidhara et al. Overall, the throughput of the system is improved at minimal complexity without yielding significant additional hardware cost. Hence, ACCP shows better overall performance in managing the hardware overhead compared to the UCP scheme.
Among the top 10 leading causes of mortality, tuberculosis (TB) is a chronic lung illness caused by a bacterial infection. Due to its efficiency and performance, using deep learning technology with FPGA as an accelerator has become a standard application in this work. However, considering the vast amount of data collected for medical diagnosis, the average inference speed is inadequate. In this scenario, the FPGA speeds the deep learning inference process enabling the real-time deployment of TB classification with low latency. This paper summarizes the findings of model deployment across various computing devices in inferencing deep learning technology with FPGA. The study includes model performance evaluation, throughput, and latency comparison with different batch sizes to the extent of expected delay for real-world deployment. The result concludes that FPGA is the most suitable to act as a deep learning inference accelerator with a high throughput-to-latency ratio and fast parallel inference. The FPGA inferencing demonstrated an increment of 21.8% in throughput while maintaining a 31% lower latency than GPU inferencing and 6x more energy efficiency. The proposed inferencing also delivered over 90% accuracy and selectivity to detect and localize the TB.
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