The method for creating a two-element multiple-input and multiple-output (MIMO) ultra-wideband hammer antenna is presented in this paper. It has been suggested to investigate characteristics of this ultra-wideband antenna design. Split-ring resonators (SRR), made of metamaterial, have enhanced performance of antenna in terms of multiplexing effectiveness, S parameters, radiation characteristics, envelope correlation coefficient, and diversity gain. Reduced weight and size of this antenna technology make it easier to integrate into a 5G and linked object receiver. Because the outputs from the previous designer to the target entity did not satisfy the standards, we thoroughly researched design attributes and made parametric changes to the design to reach the precise outcomes.

In this paper, memory optimization and architectural level modifications are introduced for realizing the low power <span lang="EN-US">residue number system (RNS) with improved flexibility for electroencephalograph (EEG) signal classification. The proposed RNS framework is intended to maximize the reconfigurability of RNS for high-performance finite impulse response (FIR) filter design. By replacing the existing power-hungry RAM-based reverse conversion model with a highly decomposed lookup table (LUT) model which can produce the results without using any post accumulation process. The reverse conversion block is modified with an appropriate functional unit to accommodate FIR convolution results. The proposed approach is established to develop and execute pre-calculated inverters for various module sets. Therefore, the proposed LUT-decomposition with RNS multiplication-based post-accumulation technology provides a high-performance FIR filter architecture that allows different frequency response configuration elements. Experimental results shows the superior performance of decomposing LUT-based direct reverse conversion over other existing reverse conversion techniques adopted for energy-efficient RNS FIR implementations. When compared with the conventional RNS FIR design with the proposed FSM based decomposed RNS FIR, the logic elements (LEs) were reduced by 4.57%, the frequency component is increased by 31.79%, number of LUTs is reduced by 42.85%, and the power dissipation was reduced by 13.83%.</span>

This paper presents the improvements in the combined solution for the noise estimation and the speech enhancement in digital hearing aids in time domain. This study focuses on the single channel statistical temporal speech enhancement using adaptive Wiener filtering. In this technique, the noise is updated based on the short-term uncleaned signal to noise threshold ratio (ST-USNTR) of the frame. It works best if and only if the back ground noise level is low compared to that of speech of interest. We considered the time domain algorithms in order to consider the time varying nature of speech signal. The performance of the proposed algorithm is evaluated for speech signal with seven ty pes of noises and three signal to noise ratios (SNR) levels in each type of noise. From the results, it is clear that the basic level of adaptive speech enhancement is obtained using statistical parameters of noisy speech without the need for reference input.