A Novel Method of Modeling the Electromagnetic Spectrum Management System for the Fleet

2007 ◽  
Author(s):  
Yu Jing ◽  
Liao Rongtao ◽  
Wen Dinge
Keyword(s):  
Management System ◽  
Spectrum Management ◽  
Electromagnetic Spectrum ◽  
Novel Method

Novel application of a fecal management system for vaginal use in radiotherapy- associated rectovaginal fistula

2012 ◽  
Vol 27 (1) ◽  
pp. 91-93 ◽  
Author(s):  
Laura A Sonoda ◽  
Rachel E Rosenheck ◽  
Katherine Tierney ◽  
Laila I Muderspach ◽  
Suzanne L Palmer ◽  
...  
Keyword(s):  
Cervical Carcinoma ◽  
Rectovaginal Fistula ◽  
Management System ◽  
Primary Treatment ◽  
Fistula Formation ◽  
Advanced Stage ◽  
Gynecologic Malignancies ◽  
Advanced Cervical Carcinoma ◽  
Novel Method ◽  
Advanced Stage Cancer

While highly effective for treating certain gynecologic malignancies, radiotherapy carries known risks, including fistula formation. We report a 75-year-old female with advanced cervical carcinoma who was provided a vaginally placed fecal management system after developing a rectovaginal fistula following primary treatment with chemoradiation. This report presents and discusses a novel method to palliate symptomatic RVFs in advanced-stage cancer.

scholarly journals Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification

2021 ◽  
Author(s):  
Anu Jagannath ◽  
Jithin Jagannath
Keyword(s):  
State Of The Art ◽  
Spectrum Management ◽  
Signal Classification ◽  
Electromagnetic Spectrum ◽  
Signal Recognition ◽  
Single Task ◽  
Communication Signals ◽  
Spectrum Monitoring ◽  
Task Learning ◽  
Wireless Signal

Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) and beyond 5G communications, Internet of Things networks, among others. State-of-the-art studies in wireless signal recognition have only focused on a single task which in many cases is insufficient information for a system to act on. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks in conjunction with multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. Additionally, we consider the problem of heterogeneous wireless signals such as radar and communication signals in the electromagnetic spectrum. Accordingly, we have shown how the proposed MTL model outperforms several state-of-the-art single-task learning classifiers while maintaining a lighter architecture and performing two signal characterization tasks simultaneously. Finally, we also release the only known open heterogeneous wireless signals dataset that comprises of radar and communication signals with multiple labels.

scholarly journals Electromagnetic Environment Portrait Based on Big Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Lantu Guo ◽  
Meiyu Wang ◽  
Yun Lin
Keyword(s):  
Smart Cities ◽  
Spectrum Management ◽  
Electromagnetic Spectrum ◽  
Dimensional Model ◽  
Spectrum Utilization ◽  
Electromagnetic Environment ◽  
Channel Utilization ◽  
Storage And Retrieval ◽  
Big Data Mining ◽  
Wireless Service

With the development of IoT in smart cities, the electromagnetic environment (EME) in cities is becoming more and more complex. A full understanding of the characteristics of past spectrum resource utilization is the key to improving the efficiency of spectrum management. In order to explore the characteristics of spectrum utilization more comprehensively, this paper designs an EME portrait model. By checking the statistical information of the spectrum data, including changes in the noise floor and channel utilization in each individual wireless service, the correlation between the spectrum and time or space of different channels and the information is merged into a high-dimensional model through consistency transformation to form the EME portrait. The portrait model is not only convenient for storage and retrieval but also beneficial for transfer and expansion, which will become an important foundation for intelligent electromagnetic spectrum management.

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