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This study proposes an innovative expert system that uses exclusively EEG signals to diagnose schizophrenia in its early stages. For diagnosing psychiatric/neurological disorders, electroencephalogram (EEG) testing is considered a financially viable, safe, and reliable alternative. Using the reconstructed phase space (RPS) and the continuous wavelet transform, the researchers maximized the differences between the EEG nonstationary signals of normal and schizophrenia individuals, which cannot be observed in the time, frequency, or time-frequency domains. This reveals significant information, highlighting more distinguishable features. Then, a deep learning network was trained to enhance the accuracy of the resulting image classification. The algorithm’s efficacy was confirmed through three distinct methods: employing 70% of the dataset for training, 15% for validation, and the remaining 15% for testing. This was followed by a 5-fold cross-validation technique and a leave-one-out classification approach. Each method was iterated 100 times to ascertain the algorithm’s robustness. The performance metrics derived from these tests — accuracy, precision, sensitivity, F1 score, Matthews correlation coefficient, and Kappa — indicated remarkable outcomes. The algorithm demonstrated steady performance across all evaluation strategies, underscoring its relevance and reliability. The outcomes validate the system’s accuracy, precision, sensitivity, and robustness by showcasing its capability to autonomously differentiate individuals diagnosed with schizophrenia from those in a state of normal health.

The anti-jamming scheme is an important issue in the modern communication system, where the spread-spectrum signals are usually adopted. In a complex electromagnetic environment where the jamming sources may be close to the communication devices, a large jamming-to-signal ratio is possible. How to achieve the desired accuracy for the communication system in the presence of strong jamming is a crucial and outstanding problem. To solve this problem, we propose a method to identify the desired signal and the nonstationary jamming signal via Lagrangian Coherent Structures (LCS), and a new algorithm for calculating the maximum Finite-Time Lyapunov Exponent (FTLE) is presented. As opposed to the current approach which requires a vector field in the state space at each instant time, the proposed algorithm is performed on the vector fields which are respectively built on the desired signal and the nonstationary jamming signal via Hilbert Transform. A simulation test is conducted on a binary offset carrier (BOC) modulated signal and a chirp signal. The results indicate that the proposed method can provide new features to efficiently identify the nonstationary jamming signal.

Automatic voice pathology detection enables an objective assessment of pathologies that influence the voice production strategy. By utilizing the conventional pipeline model as well as the modern deep learning-centric end-to-end methodology, numerous pathological voice analyzing techniques have been developed. The conventional methodology is still a valid choice owing to the lack of enormous amounts of training data in the study region of pathological voice. In the meantime, obtaining higher precision, higher accuracy, and stability is still a complicated task. Therefore, by amalgamating the nonlinear measure, the pathological voices are analyzed to abate such risks. The viability of six nonlinear discriminating measures derived from the phase space realm, involving healthy and pathological voice signals, is studied in this work. The analyzed parameters are Singularity spectrum coefficients (${\alpha }_{min}$, ${\alpha }_{max},{\gamma }_1$ and ${\gamma }_2$). Correlation entropy at optimum embedding dimension ($K_{2m}$) and correlation dimension at optimum embedding dimension ($D_{2m}$). Analyzing the pathological voices with better accuracy rates is the major objective of the proposed methodology. Here, the Support Vector Machine (SVM) was utilized as the classifier. Experimentations were performed on VOiceICarfEDerico (VOICED) databases subsuming 208 healthy, as well as pathological voices, amongst these 50 samples, were utilized. Here, the model obtained 97% of accuracy with 99% as of the classifier with Gaussian kernel function. Therefore, to differentiate normal as well as pathological subjects, the six proposed characteristics are highly beneficial; in addition, they will be supportive in pathology diagnosis.