Examinando por Autor "Zhang, Qi"

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High diagnostic quality ECG compression and CS signal reconstruction in body sensor networks
(IEEE, 2016) Chidean, Mihaela I.; Barquero-Pérez, Óscar; Zhang, Qi; Jacobsen, Rune Hylsberg; Caamaño, Antonio J
Compression of electrocardiograms (ECG) in wireless environments, with diagnostic quality, has shown limited potential. This lack of quality preservation, using Wavelet Transform (WT), is due to the fact that the multiple levels of detail that can be achieved in the time domain are not exploited. In the present work, we propose to fully exploit the wavelet capability to operate at different levels of signal detail at different time scales. WT with an appropriate Compressed Sensing (CS) matrix is used in the electrode nodes of body sensor networks to encode and compress the ECG. Then, the signal is reconstructed using a basis pursuit denoise algorithm. Preservation of the diagnostic quality by means of standardized metrics is then tested for multiple wavelet bases and levels. High quality ECGs from 50 healthy patients are used to statistically show that diagnostic quality preservation is possible even at high compression rates. In these cases suitable ECG wavelets are required.
Network Traffic Characterization Using L-moment Ratio Diagrams
(2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS), 2019-10) Chidean, Mihaela I.; Carmona-Murillo, Javier; Jacobsen, Rune H.; Zhang, Qi
5G networks are facing to new challenges related to the growing traffic volume and service diversity. Some of the major concerns in this new scenario are the security and privacy issues required for a full technology adoption. Traffic characterization is a compound of strategies intended to define formally the behaviour and patterns in the Internet traffic. In this work, we propose the use of statistical features of network flows to characterize some of the most common attacks in the current networks through the L-moment ratio diagrams. Our work identify the parameters that can discriminate normal from malicious traffic. Moreover, our preliminary results show that this technique enables the differentiation of anomalies and can also identify several types of attack traffic.