Speaker 1 : Nan Wang

Title : Optimal Threshold of Welch’s Periodogram for Sensing OFDM Signals at Low SNR Levels


Spectrum sensing is one of the key technologies to realize dynamic spectrum access in cognitive radio systems, especially being able to reliably detect primary user signal in low signal-to-noise ratio (SNR) levels. In this research, a new optimal threshold setting algorithm based on the conventional Welch¡¯s energy detection algorithm is proposed to achieve an efficient trade-off between the detection probability and false alarm probability for OFDM signals at the low SNR levels. The proposed optimal threshold algorithm in the Welch¡¯s method demonstrates a better spectrum sensing performance at the low SNR levels. The relationship between the spectrum utilization and optimal threshold is derived. The effect of spectrum utilization on the performance of spectrum sensing is also analyzed.

Speaker 2 : Roya Haratian

Title : Towards Flexibility in Sensor Placement for Body Area Sensor Networks: A Signal Processing Approach


Human body motion can be captured by body area sensor networks. Accurate sensor placement with respect to anatomical landmarks is one of the main factors determining the accuracy of motion-capture systems. Changes in position of the sensors cause increased variability in the motion data, so isolating the characteristic features that represent the most important motion patterns is our concern. As accurate sensor placement is time-consuming and hard to achieve we propose a signal processing technique that can enable salient data to be isolated.  By using functional Principal Component Analysis (f-PCA) we compensate for the variation in data due to changes in the on-body positioning of sensors. More precisely, we investigate the use of f-PCA for filtering and interpreting motion data, whilst accounting for variability in the sensor origin. Data are collected through a marker-based motion capture system from two designed experiments based on human body and robot arm movement. Results show differences between similar actions across different sessions of marker wearing with random changes in position of sensors. After applying the f-PCA filter on the data, we show how uncertainties due to sensor position changes can be compensated for.

Date: 5th  June, 2013.

Time: 14.00-15.00 hrs

Venue:  QMUL Maths:1.03

2013 June 5- Nan Wang & Roya Haratian