Technologies have advanced rapidly in the design of filters to enhance effectiveness of Signal to Noise Ratio for mobile communications including SAR applications. In this research work Butter worth active band pass filter for 2 to 6 GHz was designed using XILINX and MATLAB software’s. This was optimized, analyzed and evaluated keeping the sampling frequency at 48 GHz and Kiser window for 0.5 Beta. Further to verify the simulated results hardware circuitry is also designed based on SPARTAN-3 FPGA –development kit. Asb) part of this research work 7 -9 KHz Butter worth active band pass filter was designed using active components which was put into circuit, tested by passing a sinusoidal test signal along with noise and the filtered output signals are presented. Based on these experimental results conclusions have been drawn for 2 to 6 GHz filter for SAR applications. It is observed that when signals are received with heavy noise the coefficients of filters are selected in such way that the noise content can be minimized to great extent.
Technologies have advanced rapidly in the field of digital signal processing due to advances made in high speed, low cost digital integrated chips. These technologies have further stimulated ever increasing use of signal representation in digital form for purposes of transmission, measurement, control and storage. Design of digital filters especially adaptive or semi adaptive is the necessity of the hour for SAR applications. The aim of this research work is to design and evaluate the performance of 380-400 KHz Bartlett, Blackman and Chebyshev digital semi adaptive filters. XILINX and MATLAB software are used for designing of these filters. As part of practical research work these designs were translated using FPGA hardware SPARTAN-3E kit. Blackman and Chebyshev filters designed using software and hardware were tested by a sinusoidal test signal of 381 KHz along with noise and the filtered output signals are presented. These designs were optimized, analyzed, compared and evaluated keeping the sampling frequency at 5 MHz for 64 orders.
Summary Nowadays, wireless sensor networks (WSNs) have paid huge attention among researchers due to their wide applications. WSNs possess multiple sensor nodes that transmit data to each other by using constrained energy resources. The sensor nodes are highly affected by collision due to the transmission of packets over the network by one or two nodes at the same time. Collision detection is necessary to increase network security and enhance the lifetime of sensor nodes. In most of the previous research, efficiently implementing collision detection algorithms while minimizing resource usage remains a significant challenge. Thus, a hybrid deep learning model deep Kronecker recurrent neural network (DKRNN) is developed in this research. Here, the cluster head is selected using the chronological skill optimization algorithm (CSOA) algorithmic approach by considering multi‐objective parameters like energy, distance, delay, and trust. The network‐based parameters are then extracted from the network. Later, the collision is detected using the DKRNN approach and the collision is mitigated finally using a packet pre‐scheduling model named Dolphin Ant Lion Optimization (Dolphin ALO). Moreover, the detection performance of CSOA+ DKRNN is validated, and it achieved superior performance with a collision detection rate (CDR) of 0.940, packet delivery ratio (PDR) of 0.660, throughput of 0.850Mbps, and energy consumption of 0.110 J.
Technologies have advanced rapidly in the field of digital signal processing due to advances made in high speed, low cost digital integrated chips. These technologies have further stimulated ever increasing use of signal representation in digital form for purposes of transmission, measurement, control and storage. Design of digital filters especially adaptive or semi adaptive is the necessity of the hour for SAR applications. The aim of this research work is to design and performance evaluation of 7.8 – 8.2 GHz Bartlett, Blackman and Chebyshev digital semi adaptive filters. For this work XILINX and MATLAB softwares were used for the design. As pert of practical research work these designs were translated using FPGA hardware SPARTAN-3E kit. These were optimized, analyzed, compared and evaluated keeping the sampling frequency at 20 GHz for 64 order. These filters designed using software were tested by passing a sinusoidal test signal of 8 GHz along with noise and the filtered output signals are presented. Signal to Noise ratios were evaluated, plotted and comparative analysis carried out in this paper.
In recent years, adaptive filtering has become one of the effective and popular approaches for the processing and analysis of the signals with noise especially of the biomedical signals. Adaptive filters permit to detect time varying potentials and to track the dynamic variations of the signals. Besides, they modify their behavior according to the input signal. Therefore, they can detect shape variations in the ensemble and thus they can obtain a better signal estimation. The aim of this paper is to study, analyze various adaptive filter algorithms and apply Mat lab to investigate their performance behaviors with two step sizes of 0.02 and 0.04. Further to remove motion artifacts from Electrocardiogram signal as an application of this concepts. At the end of this paper, a performance study has been done between these algorithms based on various step sizes. It has been found that there will be always tradeoff between step sizes and Mean square error. The Electrocardiogram signals used in this paper are from the MIT-BIH database. Elimination of noises from Electrocardiogram signal example is a classical problem.
Technologies have advanced rapidly in the field of digital signal processing due to advances made in high speed, low cost digital integrated chips. These technologies have further stimulated ever increasing use of signal representation in digital form for purposes of transmission, measurement, control and storage. Design of digital filters especially adaptive or semi adaptive is the necessity of the hour for SAR applications. The aim of this research work is to design and performance evaluation of 380-400 KHz Bartlett, Blackman and Chebyshev digital semi adaptive filters. For this work XILINX and MATLAB softwares were used for the design. As pert of practical research work these designs were translated using FPGA hardware SPARTAN-3E kit. These were optimized, analyzed, compared and evaluated keeping the sampling frequency at 5 MHz for 64 order. Both these filters designed using software and hardware were tested by passing a sinusoidal test signal of 381 KHz along with noise and the filtered output signals are presented.
'%3e%3ccircle r='20' fill='%23008'/%3e%3ccircle r='17.5' fill='%23fff'/%3e%3ccircle r='3.5' fill='%23008'/%3e%3cg id='d'%3e%3cg id='c'%3e%3cg id='b'%3e%3cg id='a' fill='%23008'%3e%3ccircle r='.875' transform='rotate(7.5 -8.75 133.5)'/%3e%3cpath d='M0 17.5.6 7 0 2l-.6 5L0 17.5z'/%3e%3c/g%3e%3cuse xlink:href='%23a' transform='rotate(15)'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='rotate(30)'/%3e%3c/g%3e%3cuse xlink:href='%23c' transform='rotate(60)'/%3e%3c/g%3e%3cuse xlink:href='%23d' transform='rotate(120)'/%3e%3cuse xlink:href='%23d' transform='rotate(-120)'/%3e%3c/g%3e%3c/svg%3e)
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
