Neural network-aided receivers for soliton communication impaired by solitonic interaction

In this paper, different neural network-based methods are proposed to improvethe achievable information rate in amplitude-modulated soliton communication systems. The proposed methods use simulated data to learn effective soliton detection by suppressing nonlinear impairments beyond amplifier noise, including intrinsic inter-soliton interaction, Gordon-Haus effect-induced timing jitter, and their combined impact. We first present a comprehensive study of these nonlinear impairments based on numerical simulations. Then, two neural network designs are developed based on a regression network and a classifier. We estimate the achievable information rates of the proposed learning-based soliton detection schemes as well as two modelbased benchmark schemes, including the nonlinear Fourier transform eigenvalue estimation and continuous spectrum-aided eigenvalue estimation schemes. Our results demonstrate that bothlearning-based designs lead to substantial performance gains when compared to the benchmark schemes. Importantly, we highlight that exploiting the channel memory, introduced by solitonic interactions, can yield additional gains in the achievable information rate. Through a comparative analysis of the two neural network designs, we establish that the classifier design exhibits superioradaptability to interaction impairment and is more suitable for symbol detection tasks in the context of the investigated scenarios

Potential Benefits and Limitations of Using Virtual Reality-Based Patient Simulation Systems in Education

Introduction: The utility of capabilities of technologies as Virtual Reality (VR) and Virtual Patient Simulators (VPS) has provided rich opportunities in medical education. This study endeavored to solicit the interns and final-year medical residents' views on the benefits and potential limitations of applying these technologies in training of diagnostic and surgical procedures. Methods: In this qualitative study with a content analysis approach based on purposeful sampling, 100 interns, and final-year residents from Tehran University of Medical Sciences participated in the academic year 2018. Sampling was continued until data saturation was reached. A researcher-made questionnaire with open-end questions was employed to collect related data. The collected data were analyzed through the content analysis method. Results: The results revealed that the identified benefits generally included increasing educational opportunities, facilitating knowledge transfer, acquisition of experience and skills, technology dynamics, and effective learning. Besides, potential limitations of VPS included program standardization challenges, ethics and professional commitment, content design limitations, the unreliability of artificial diagnostic and surgery processes, cost, and sustainable support constraints. On the other hand, as executive considerations, the direct participation of clinical teams in all processes related to the development, evaluations, and quality assurance of standard programs, as well as receiving continuous feedback from students and finally the need to update programs, have been considered important. Conclusion: Given the benefits, it seems indispensable to plan to invest, develop, and implement VR-based training assistance programs considering ways to reduce potential constraints. More targeted use of this technology, especially during the COVID-19 epidemic to continuity and dynamism of the learning processes is recommended