In this paper, the impact of the dispersion effect, due to atmospheric pressure and temperature, on NRZ-OOK terrestrial freespace optical transmission system is investigated. An expression for the dispersion parameter in FSO atmospheric channel is derived. The results show that the variation of the refractive index along the transmission path induces fluctuations of group velocity dispersion of the optical pulse resulting in broadening of the pulse duration. Simulation results show that at a propagation distance of 7.5 km, the broadening ratio for input pulse duration of 300 fs is approximately 2.39. Further, at a propagation distance of 7.5 km, the remaining fraction of energy is approximately 40 % for a 300 fs input pulse duration. However, by increasing the transmitter input power, the effect of dispersion could be reduced. Namely, for a reference BER of 10-9, the maximum distance that it could be achieved is about 1.461 km for an input power of 1 mW, while it is about 2.694 km for an input power of 4 mW. The results indicate that the effect of dispersion due to pressure and temperature increases with the propagation distance. What induces a high BER. However, the results show that it is possible to reach longer propagation distances with a lower BER by increasing the input power.

In this paper, we have investigated the FSO communication capacity in the presence of the temporal pulse broadening effect which caused by the group velocity dispersion and atmospheric turbulence. We have modeled the pulse propagation in atmospheric turbulence and chromatic dispersion by using the two-frequency mutual coherence function (MCF). By using the Shannon-Hartly theorem, we have derived an analytical expression for signal to noise ratio of the propagating Gaussian pulse in dispersive and turbulent atmospheric channel. The results shows that the capacity communication degrades drastically under the temporal pulse broadening effect. The degradation is very important for a longer distance. By increasing the power of the transmitted signal, and choosing the appropriate transceiver altitude, we can minimize the pulse-broadening effect on FSO capacity.

Brain imaging is a critical tool in mental health diagnostics, particularly for detecting anomalies associated with behavioral disorders, anxiety, depression, and neuropsychiatric conditions. Traditional deep learning approaches, such as Convolutional Neural Networks (CNNs), have shown promise in analyzing functional Magnetic Resonance Imaging (fMRI), but they often suffer from computational limitations, feature extraction inefficiencies, and generalization issues. Quantum Machine Learning (QML) offers a transformative solution by enhancing feature representation, improving classification accuracy, and exploiting the power of quantum computation for large-scale medical imaging datasets. In this paper, we explored the potential of quantum models for detecting anomalies in brain imaging. Using the UCLA Consortium for Neuropsychiatric Phenomics dataset, we compared quantum models, including the Variational Quantum Classifier (VQC), Quantum Convolutional Neural Network (QCNN), and Quantum Support Vector Machine (QSVM), with classical models like CNNs and the Support Vector Machine (SVM). Our results revealed that QSVM and VQC outperformed classical models, both achieving 92% accuracy and perfect recall (100%), making them highly effective at identifying positive cases-a critical requirement in clinical diagnostics. QCNN, while less accurate at 74%, still showed strong precision (90%) and an F1score of 85%, highlighting its potential where avoiding false positives is key. Classical models, in comparison, performed moderately well: SVM reached 87% accuracy and CNN achieved 83%, but both had lower precision and F 1 -scores, suggesting less consistent performance. These findings suggest that quantum models, especially VQC and QSVM, offer a promising advantage in neuroimaging-based mental health diagnostics.