Field programmable gate arrays (FPGAs) are becoming increasingly important implementation platforms for digital circuits. This paper focuses on the implementation of Adaptive Infinite Impulse response (IIR) filter on an FPGA using Modified Particle Swarm Optimization (PSO) Algorithm. The proposed Filter is capable of finding the global optimum solution for system identification problem in less number of iterations. The modified PSO algorithm has been developed and simulated using MATLAB. The result shows the enhanced speed of purposed design in terms of number of iterations it takes to identify the unknown system. The same algorithm has also been realized on various Xilinx FPGA devices and performances have also been analyzed. The area utilization by the proposed design on different FPGA devices has been compared. The results show that proposed filter is consuming very less area in terms of LUTs and Slices to provide enhanced area efficiency.
The wireless body area network (WBAN) is a branch of the wireless sensor network (WSN) intended for tracking essential patients’ physiological signals and transferring this knowledge to the coordinator. During the routing of data, WBANs encounter critical routing problems like WSNs. Moreover, the particular constraints of WBANs make it more challenging that they need to be addressed. This survey article categorizes and briefly analyzes a range of current routing methods utilized in WBANs. The routing protocol is essential to the creation of any efficient and reliable wireless body area network due to a limited size of battery in the body sensor nodes. A comparison study of numerous routing protocols has been made in this paper, which is helpful in selecting the optimal routing protocol for the application being targeted. The article describes the WBAN architecture and addresses numerous challenges in the context of successful WBAN routing. In this paper, several existing WBAN routing methods are presented which are influenced by network structure, energy, quality of service (QoS), node temperature, human position, node transmission range, and other factors. The protocols, including cross-layered, cluster-based, QoS-aware, postural movement-based, temperature-aware, postural movement-based, and routing protocols, are given in an expressive taxonomy. Different routing protocols that have already been developed for WBANs are compared with more advanced protocols. The pros and cons of each protocol are looked at based on factors like delay, packet delivery ratio, and energy use. The researchers can utilize this survey paper as a reference for studying various routing protocols particularly in the medical and healthcare systems.
Higher charge mobility, gate control, and better electrostatics are the key reasons that make a carbon nanotube field effect transistor (CNTFET) a better candidate as the successor of conventional complementary metal oxide semiconductor transistors. However, the increased charge mobility also enhances the leakage power. This work uses CNTFET for designing a low-power eight-transistor static random access memory (8T SRAM) cell. The leakage power of the proposed cell is reduced by 2.21× compared to conventional 6T SRAM at 0.3 V with similar CNTFET parameters. The read and write power delay product of the proposed design is improved by 1.02× and 1.85×, respectively. Moreover, the read/write/ hold static noise margin of the proposed cell is also enhanced by 1.98×/ 0.99×/ 1.01×, respectively, compared to the conventional 6T design. The proposed cell is also compared with three already proposed CNTFET based 8T SRAM designs. Cadence Virtuoso simulation tool and Stanford University 32 nm CNTFET verilog-A model file are used to achieve simulation results.
The concept of "smart agriculture" relies on the integration of sensors within an Internet of Things (IoT) network. Machine learning (ML) algorithms are integrated at various levels of the IoT system design to augment its functionality and enhance its capability. This article is a bibliometric review of 42 articles published between 2018 and 2022 using the Web of Science database. The results of the review showed an exponential growth in the use of ML algorithms in IoT systems for different agriculture applications. Additionally, two key research questions are addressed in this article, one being the development of IoT-ML-enabled smart agriculture over the past five years, and the second being the main research gaps for applications of machine learning and IoT in smart agriculture. The article concludes with a discussion of the results and future directions for research in the field.
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