This manuscript presents a new technique to improve the performance of adaptive filters in handling the non-Gaussian or impulsive noise environment. The conduct of the adaptive filter decays in the presence of impulsive noise or outliers. To improve the efficiency of the filtering technique, this work presents a robust exponential hyperbolic tangent Geman McClure function for nonlinear system identification. The algorithm utilizes the saturation properties of the hyperbolic tangent function to improve the performance under impulsive noise. The simulation results clearly illustrate the potency of the suggested technique.

The hybrid selective fixed-filter active noise control with filtered reference normalized least mean square (SFANC-FxNLMS) method struggles in dynamic noise environments due to its reliance on static filters, which limits effectiveness when noise characteristics change rapidly. The generative fixed-filter active noise control with Kalman filtering (GFANC-Kalman) approach offers improved adaptability by dynamically adjusting the filtering process but may still underperform in complex noise scenarios. The dynamic fixed-filter active noise control with extended Kalman filter (DFANC-EKF) method overcomes these limitations by integrating an extended Kalman filter with a 2D convolutional neural network for advanced feature extraction. This integration enables the system to better capture and adapt to intricate noise patterns, significantly enhancing noise reduction. Numerical simulations using real-world noise data validate the DFANC-EKF approach's superior performance across various challenging scenarios.

The noise cancellation performance of an active control system decreases when there are temporal variations in the primary and secondary paths. An active noise control (ANC) framework has been introduced in this work, which incorporates four adaptive filters and two decorrelation filters for online secondary path modelling. A novel adaptive algorithm for an active noise control filter has been developed with the combination of modified gain filtered-x recursive least square and normalised step size filtered-x least mean square. The aim is to improve the reduction of mean noise and decrease residual noise while maintaining consistent convergence rate. To update the decorrelation filters in the framework, an adaptive variable step size modified decorrelation normalised least mean square algorithm has been used. These filters are designed to maximize the efficiency of secondary path modelling. Compared to its counterparts, the simulation results illustrate the enhancements of the proposed framework without a substantial increase in overall computational complexity.

A new method is suggested to improve the effectiveness of adaptive filters in dealing with unexpected disturbances at the error sensor. This method focuses specifically on the difficult scenario of α-stable noise in feedback cancellation for hearing aids. α-stable noise, which is distinguished by its strong tails and abrupt behavior, poses considerable difficulties for conventional adaptive algorithms. In order to tackle this issue, we propose the implementation of a fractional order hyperbolic tangent (FOHT) algorithm. Fractional order systems, which utilize non-integer order derivatives to represent intricate dynamics, provide improved adaptability and precision, especially in settings where non-Gaussian noise, such as α-stable distributions, is prevalent. The algorithm utilizes the distinct characteristics of fractional calculus to enhance the resilience and adaptability of the system, thereby reducing the influence of α-stable noise. The results of extensive simulations indicate that the FOHT algorithm outperforms existing techniques in terms of steady-state convergence and robustness.

The issue of acoustic feedback poses a consistent challenge in the context of hearing aids since it imposes restrictions on the attainable amplification levels and has the potential to significantly diminish the perceptual audio quality through the generation of whistling sounds. In recent studies, researchers have employed delay-less multiband structured band-wise acoustic feedback alleviation techniques for hearing aids. However, the resilience of this system in the existence of non-Gaussian noise has yet to be thoroughly addressed. To tackle this matter, a robust and new cost function has been introduced based on the modification of the von Mises distribution with a scaling parameter. In addition, fractional lower-order models provide resilience to heavy-tailed distributions, enhanced precision, heightened versatility and suitability for diverse data formats. In light of this viewpoint, a robust fractional order von Mises-based subband acoustic feedback compensation technique is introduced for hearing aids. The method’s efficacy has been evaluated by computer simulations involving speech and music signals at various signal-to-noise ratio (SNR) levels. The evaluation results show 2-3 dB decrease in misalignment and 2-3 dB enhancement in added stable gain in contrast to prior methodologies. Moreover, perceptual evaluation of speech quality and hearing aid speech quality indexes have been improved by ≈ 2 % and ≈ 10 % respectively as employed with the proposed technique.

In the pursuit of enhancing the robustness of adaptive filters against spurious disturbances at the error sensor in the case of feedback cancellation in hearing aids, a novel logarithmic Champernowne function is proposed as a resilient norm. This leads to the development of a robust logarithmic Champernowne adaptive filter (LCMAF) with a prediction error method that is capable of effectively mitigating the impact of such disturbances. Additionally, simulations of the suggested algorithm were run, and the results clearly show that, in comparison to the earlier approaches, the present algorithm performs better in steady-state convergence.

The capacity of Elastic Optical Network (EON) to dynamically employ network resources is drawing a lot of interest these days. Access to spectrum resources is challenging for new requests especially when the spectrum is divided into numerous tiny segments. The distribution of spectrum, intelligent and effective routing are two other major EON challenges. In order to allocate contiguous aligned spectrum slots to multiple requests, this includes searching for a certain route. EON seeks to maximize connectivity while utilizing the least amount of spectrum resources possible in this manner. Given that dispersion is a significant physical problem in optical networks, this research investigated a method of spectrum distribution using first random fit (FRF) method. This plan takes dispersion into account while performing the spectrum allocation strategy. This system tends to allocate more connection requests and use spectrum more effectively when using a FRF strategy. This technique employs and assigns the spectrum slot to the longest photonic route starting from the lowest indexed slot using the First Fit algorithm while using a less reliable modulation technique like BPSK. Using a more stable modulation technology (QPSK) in combination with a random fit assignment method, it is possible to utilize the next higher indexed spectrum spot which has a bigger impact on dispersion to the shortest photonic path. Advantage of using a heuristic approach for large scale network can be quantitatively measured in terms of computational efficiency and scalability. Till now, as per our knowledge FRF technique with ILP approach considering dispersion as one of the parameter has not been covered yet. The finding demonstrate that the suggested method outshines the previous distance adaptive and non distance adaptive spectrum allocation schemes in terms of distinct parameters as fragmentation (0.9966), CASR (0.0033), blocking probability (BBP) of 0.048, blocking probability (B.P) and optimality gap of 4.71 % and other parameters.