Abstract: Structural failure is caused mainly by damage that
often occurs on structures. Many researchers focus on to obtain very
efficient tools to detect the damage in structures in the early state. In
the past decades, a subject that has received considerable attention in
literature is the damage detection as determined by variations in the
dynamic characteristics or response of structures. The study presents
a new damage identification technique. The technique detects the
damage location for the incomplete structure system using output
data only. The method indicates the damage based on the free
vibration test data by using ‘Two Points Condensation (TPC)
technique’. This method creates a set of matrices by reducing the
structural system to two degrees of freedom systems. The current
stiffness matrices obtain from optimization the equation of motion
using the measured test data. The current stiffness matrices compare
with original (undamaged) stiffness matrices. The large percentage
changes in matrices’ coefficients lead to the location of the damage. TPC technique is applied to the experimental data of a simply
supported steel beam model structure after inducing thickness change
in one element, where two cases consider. The method detects the
damage and determines its location accurately in both cases. In
addition, the results illustrate these changes in stiffness matrix can be
a useful tool for continuous monitoring of structural safety using
ambient vibration data. Furthermore, its efficiency proves that this
technique can be used also for big structures.
Abstract: In this study we focus on improvement performance
of a cue based Motor Imagery Brain Computer Interface (BCI). For
this purpose, data fusion approach is used on results of different
classifiers to make the best decision. At first step Distinction
Sensitive Learning Vector Quantization method is used as a feature
selection method to determine most informative frequencies in
recorded signals and its performance is evaluated by frequency
search method. Then informative features are extracted by packet
wavelet transform. In next step 5 different types of classification
methods are applied. The methodologies are tested on BCI
Competition II dataset III, the best obtained accuracy is 85% and the
best kappa value is 0.8. At final step ordered weighted averaging
(OWA) method is used to provide a proper aggregation classifiers
outputs. Using OWA enhanced system accuracy to 95% and kappa
value to 0.9. Applying OWA just uses 50 milliseconds for
performing calculation.
Abstract: The objective of this paper is to characterize the spontaneous Electroencephalogram (EEG) signals of four different motor imagery tasks and to show hereby a possible solution for the present binary communication between the brain and a machine ora Brain-Computer Interface (BCI). The processing technique used in this paper was the fractal analysis evaluated by the Critical Exponent Method (CEM). The EEG signal was registered in 5 healthy subjects,sampling 15 measuring channels at 1024 Hz.Each channel was preprocessed by the Laplacian space ltering so as to reduce the space blur and therefore increase the spaceresolution. The EEG of each channel was segmented and its Fractaldimension (FD) calculated. The FD was evaluated in the time interval corresponding to the motor imagery and averaged out for all the subjects (each channel). In order to characterize the FD distribution,the linear regression curves of FD over the electrodes position were applied. The differences FD between the proposed mental tasks are quantied and evaluated for each experimental subject. The obtained results of the proposed method are a substantial fractal dimension in the EEG signal of motor imagery tasks and can be considerably utilized as the multiple-states BCI applications.
Abstract: The mobile systems are powered by batteries.
Reducing the system power consumption is a key to increase its
autonomy. It is known that mostly the systems are dealing with time
varying signals. Thus, we aim to achieve power efficiency by smartly
adapting the system processing activity in accordance with the input
signal local characteristics. It is done by completely rethinking the
processing chain, by adopting signal driven sampling and processing.
In this context, a signal driven filtering technique, based on the level
crossing sampling is devised. It adapts the sampling frequency and
the filter order by analysing the input signal local variations. Thus, it
correlates the processing activity with the signal variations. It leads
towards a drastic computational gain of the proposed technique
compared to the classical one.