Radio communication systems are very widely present in our current smart lifestyles. It consists of two ends, which can carry the transmitter’s information to the receptor. Before installing any radio communication system, it is necessary to analyze the link resources. Hence, this analysis allows the determination of the received radio communication strength to prove if it is sufficient for the link to work correctly and assure a high quality of service. For this reason, new services and technologies are integrated. The objective of the present work is to improve the performance of the radio communication link of 4G systems. The study is based on real measurements using the drive test. The data collected by the drive test are analyzed to increase the performance of the radio communication. Based on this data analysis, recommendations and suggestions are issued for improving the radio communication link. The obtained results indicate a significant amelioration in the performance of the radio communication link.

Acoustic Echo Cancellation (AEC) is a topic that has received a great interest in recent years. However, a significant challenge remains with the problem of double-talk especially when the adaptive filter has a fast convergence rate. In this case, the double-talk detector (DTD) must reply in early stage and halt updating of the adaptive filter in order to avoid filter coefficients divergence. Indeed, a complex and inappropriate DTD can seriously affect the convergence rate of the adaptive filter and global performances of the AEC system. In this paper, an implementation of a simple and efficient DTD based on a recursive estimation of the decision variable which is resulting from the level comparison between far-end and microphone signals is proposed. The presented algorithm is then compared with the normalized cross-correlation (NCC) method which is taken as a reference in this work. In the simulation tests, the recursive least squares (RLS) algorithm is used to update the adaptive filter coefficients. The speech signals used in the tests are taken from the TIMIT database.

The supervision task of industrial systems is vital, and the prediction of damage avoids many problems. If any system defects are not detected in the early stage, this system will continue to degrade, which may cause serious economic loss. In industrial systems, the defects change the behaviour and characteristics of the vibration signal. This change is the signature of the presence of the defect. The challenge is the early detection of this signature. The difficulty of the vibration signal is that the signal is very noisy, non-stationary and non-linear. In this study, a new method for the early defect detection of a gear system is proposed. This approach is based on vibration analysis by finding the defect’s signature in the vibration signal. This approach has used the autocorrelation of Morlet wavelet transforms (AMWT). Firstly, simulation validation is introduced. The validation of the approach on a real system is given in the second validation part.

In this paper, a type-2 fuzzy-based adaptive sliding mode power system controller is proposed for damping low-frequency oscillations with the aim to enhance power system stability despite model uncertainties introduced by variations of system parameters and external disturbances. Addressing these latter, type-2 fuzzy systems approximating properties are used to approximate unknown power system non-linear dynamics. Furthermore, to achieve more robustness, the proposed controller design is combined with sliding mode approach. The latter and Lyapunov synthesis approach are incorporated in an adaptive fuzzy control scheme such that the derived controller is robust, closely tracking any changes in power system operating conditions and guaranteeing stability while a PI control term is added to mitigate chattering. Proposed stabiliser robustness has been tested on a single machine infinite bus system and a multi-machine power system. Non-linear simulation studies show good performance of the proposed stabiliser and confirm its superiority over conventional PSS and some other types of power stabilisers.

Non-invasive fetal electrocardiogram (fECG) extraction is still a challenging task due to the overbearing preponderance of the maternal ECG (mECG) and the presence of noise and interferences. Adaptive filtering techniques, particularly the Recursive Least Squares (RLS) algorithm, have been shown to work well for this issue. However, RLS performance largely depends on a few of its parameters (filter order, forgetting factor, and regularization term), typically tuned empirically, thus limiting robustness and generalizability. In this work, we introduce an automatic parameter optimization process based on the Particle Swarm Optimization (PSO) algorithm. The proposed method was validated using simulated signals generated on MATLAB, including abdominal recordings (aECG), maternal thoracic signals (mECG), and a reference fECG for comparison. Quantitative outcomes, using Mean Squared Error (MSE) and Signal-to-Noise Ratio (SNR) metrics, indicate that PSO-based optimization improves the quality of the resultant fECG compared to the optimal empirical settings, eliminating residual maternal interference. These findings show the potential of PSO for robust fECG extraction and its potential feasibility for real clinical data in the future.

This paper aims to implement and evaluate performances of adaptive filters applied to linear systems identification on a dsPIC platform. To do so, we considered two different applications: the identification of DC motor transfer function and the acoustic echo cancellation. For the DC motor case, real-time identification is not necessarily required. However, for the acoustic echo cancellation case, real-time identification is crucial, making execution time a key parameter in this scenario. We have implemented three adaptive algorithms: LMS, NLMS, and RLS. The first two are known for their computational simplicity, while RLS is recognized for its convergence speed. Performances evaluation is primarily based on accuracy and computation speed. A comparison is carried out in both cases to determine which algorithm is more suitable for each application. The results obtained showed that RLS is better suited for the DC motor case, while NLMS is more suitable for acoustic echo cancellation, particularly when the impulse response of the echo path is long.