In this study a highly flexible microwave shielding material was fabricated by solution casting method utilizing Nickel and biocarbon particles in PVA matrix and characterized for mechanical, magnetic, and microwave shielding properties. The main aim of this study was to prove the significant role of magnetic particles in electromagnetic interference (EMI) shielding along with conductive particles. The results show that the addition of Ni-biocarbon hybrid particle increases the shielding properties up to 56.5 dB at 20 GHz. The magnetic permeability increased gradually with the inclusion of Ni particles with a highest magnetization, coercivity, and retentivity of 1250 E À6 emu, À9000 G, and 1100 E À6 emu. Similarly the mechanical results show that adding biocarbon enhances the composite's mechanical properties. A highest tensile strength, tear strength, elongation, and hardness are noted as 38, 168 MPa, 18.4%, and 36 Shore-D. Comparatively, the hardness and elongation% of composite designations contains 3 and 5 vol% of hybrid particles have increased by 9% and 26%, respectively, in comparison to composite containing only 5 vol% of biocarbon with PVA. Scanning electron microscope fractography indicates biocarbon particles reduce voids and improve adhesion. These flexible EMI shielding composites could be used in telecommunication and other wave transmitting devices in engineering applications.

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Cognitive Radio (CR) effectively involved in the management of spectrum to perform improved data transmission. CR system actively engaged in the data sensing, learning and dynamic adjustment of radio spectrum parameters with management of unused spectrum in the signal. The spectrum sensing is indispensable in the CR for the management of Primary Users (PUs) and Secondary users (SUs) without any interference. Spectrum sensing is considered as the effective adaptive signal processing model to evaluate the computational complexity model for the signal transmission through Matched filtering, Waveform and Cyclostationary based Energy sensing model. Cyclostationary based model is effective for the energy based sensing model based on unique characteristics with estimation of available channel in the spectrum to extract the received signal in the PU signal. Cyclostationary based model uses the spectrum availability without any periodic property to extract the noise features. This paper developed a Adaptive Cross Score Cyclostationary (ACSCS) to evaluate the spectrum sensing in the CR network. The developed ACSCS model uses the computational complexity with estimation of Signal-to-Interference-and-Noise Ratio (SINR) elimination of cost function. ACSCS model uses the Adaptive Least square Spectral Self-Coherence Restoral (SCORE) with the Adaptive Cross Score (ACS) to overcome the issues in CR. With the derived ACSCS algorithm minimizes the computational complexity based on cost function compared with the ACS algorithm. To minimize the computational complexity pipeline triangular array based Gram-Schmidt Orthogonalization (GSO) structure for the optimization of network. The simulation performance analysis with the ACSCS scheme uses the Rician Multipath Fading channel to estimate detection probability to sense the Receiver Operating Characteristics, detection probability and probability of false alarm using Maximum Likelihood (ML) detector. The ACSC model uses the Square-law combining (SLC) with the moment generation function in the multipath fading channel for the channel sensing with reduced computational complexity. The simulation analysis expressed that ACSC scheme achieves the maximal detection probability value of 1. The analysis expressed that proposed ACSC scheme achieves the improved channel estimation in the 4G communication environment.