Welch FFT Segment Size Selection Method for Spectrum Awareness System

2016 ◽  
Vol E99.B (8) ◽  
pp. 1813-1823 ◽  
Author(s):  
Hiroki IWATA ◽  
Kenta UMEBAYASHI ◽  
Samuli TIIRO ◽  
Janne J. LEHTOMÄKI ◽  
Miguel LÓPEZ-BENÍTEZ ◽  
...  
Keyword(s):  
Selection Method ◽  
Size Selection ◽  
Segment Size ◽  
Awareness System

scholarly journals Welch FFT Segment Size Selection Method for FFT Based Wide Band Spectrum Measurement

2018 ◽  
Vol E101.B (7) ◽  
pp. 1733-1743
Author(s):  
Hiroki IWATA ◽  
Kenta UMEBAYASHI ◽  
Janne J. LEHTOMÄKI ◽  
Shusuke NARIEDA
Keyword(s):  
Wide Band ◽  
Selection Method ◽  
Band Spectrum ◽  
Size Selection ◽  
Segment Size ◽  
Spectrum Measurement

A study on Welch FFT segment size selection method for spectrum awareness

2016 ◽  
Author(s):  
Hiroki Iwata ◽  
Kenta Umebayashi ◽  
Samuli Tiiro ◽  
Yasuo Suzuki ◽  
Janne J Lehtomaki
Keyword(s):  
Selection Method ◽  
Size Selection ◽  
Segment Size

A 3D-PSF Space Size Selection Method Based on Inflection Point

2017 ◽  
Vol 26 (1) ◽  
pp. 160-165
Author(s):  
Fengjuan Yang ◽  
Hua Chen ◽  
Yi Cai ◽  
Riliu Liang ◽  
Shuangyan He
Keyword(s):  
Inflection Point ◽  
Selection Method ◽  
Size Selection ◽  
Space Size

Research on computer network information security based on improved machine learning

2020 ◽  
pp. 1-12
Author(s):  
Yu Guangxu
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Information Security ◽  
Extraction Method ◽  
Situational Awareness ◽  
Rapid Development ◽  
Selection Method ◽  
Feature Extraction Method ◽  
Network Information ◽  
Awareness System

The 21st century is an era of rapid development of the Internet. Internet technology is widely used in various fields. With the rapid development of network, the importance of network information security is also highlighted. The traditional network information security technology has been difficult to ensure the security of network information. Therefore, we mainly study the application of machine learning feature extraction method in situational awareness system. A feature selection method based on machine learning is proposed to extract situational features.By analyzing whether the background of network information is safe or not, and according to the current research situation at home and abroad and the trend of Internet development, this paper tries out the practical application of machine learning feature extraction method in a certain perception system. Based on the above points, a selection method based on machine learning is proposed to extract situational features. The accuracy and timeliness of situational awareness system detection are seriously affected by the high dimension, noise and redundant features of massive network traffic data.Therefore, it is of great value to further study network intrusion detection technology on the basis of machine learning.

scholarly journals An Adaptive Window Size Selection Method for Differentially Private Data Publishing over Infinite Trajectory Stream

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Geonhyoung Jo ◽  
Kangsoo Jung ◽  
Seog Park
Keyword(s):  
Differential Privacy ◽  
Window Size ◽  
Selection Method ◽  
Traffic Information ◽  
Sensor Technology ◽  
Traffic Data ◽  
Wireless Internet ◽  
Data Utility ◽  
Size Selection ◽  
Adaptive Window

Recently, various services based on user's location are emerging since the development of wireless Internet and sensor technology. VANET (vehicular ad hoc network), in which a large number of vehicles communicate using wireless communication, is also being highlighted as one of the services. VANET collects and analyzes the traffic data periodically to provide the traffic information service. The problem is that traffic data contains user’s sensitive location information that can lead to privacy violations. Differential privacy techniques are being used as a de facto standard to prevent such privacy violation caused by data analysis. However, applying differential privacy to traffic data stream which has infinite size over time makes data useless because too much noise is inserted to protect privacy. In order to overcome this limitation, existing researches set a certain range of windows and apply differential privacy to windowed data. However, previous researches have set a fixed window size do not consider a traffic data’s property such as road structure and time-based traffic variation. It may lead to insufficient privacy protection and unnecessary data utility degradation. In this paper, we propose an adaptive window size selection method that consider the correlation between road networks and time-based traffic variation to solve a fixed window size problem. And we suggest an adjustable privacy budget allocation technique for corresponding to the adaptive window size selection. We show that the proposed method improves the data utility, while satisfying the equal level of differential privacy as compared with the existing method through experiments that is designed based on real-world road network.

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