This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio *** threshold identification method is implemented in the received signal at the secondary user...
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This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio *** threshold identification method is implemented in the received signal at the secondary user based on the square *** proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division ***,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio *** the dynamic threshold,the signal ratio-based threshold is *** threshold is computed by considering the modified black widow optimization algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and *** general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user *** limitations undermine the sensing accuracy of the energy identification ***,the ETBED technique is developed to enhance the energy efficiency of cognitive radio *** projected approach is executed and analyzed with performance and comparison *** proposed method is contrasted with the conventional techniques of theWhale optimizationalgorithm(WOA)and GreyWolf optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.
In general, designing a safe and robust automatic landing system is considered as a challenging task because most accidents occur during the landing and takeoff of aircraft. Due to the imbalance of a few external dist...
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In general, designing a safe and robust automatic landing system is considered as a challenging task because most accidents occur during the landing and takeoff of aircraft. Due to the imbalance of a few external disturbances such as atmospheric turbulence, wind gusts, low altitude, measurement noises, low speed as well as wind shears the unexpected accidents take place. So, to overcome such types of accidents during the landing of UAV and to ensure an exact landing path our paper proposes a novel HDRNN-MBWO based aircraft auto-landing system. Here hybrid deep neural networks and modified black widow optimization algorithms are integrated so as to form a novel HDRNN-MBWO approach. The main intention of the proposed approach involves designing a safe, robust as well as smooth automatic landing system. Here, the HDRNN-MBWO approach is employed to obtain better optimization performances. In addition to this, this proposed approach detects the fault and provides an estimated output with high efficiency, the smooth landing of aircraft as well as a minimum error value rate. The minimization of the error helps to prevent the crushing of aircraft during auto-landing thereby achieving smooth landing. The performance evaluation and the comparative analysis are carried out to examine the efficiency of the proposed approach under various aspects.
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