Browsing by Subject "Linear combinations"

Now showing 1 - 4 of 4

Open Access
Adaptive decision fusion based cooperative spectrum sensing for cognitive radio systems
(IEEE, 2011) Töreyin, B. U.; Yarkan, S.; Qaraqe, K. A.; Çetin, A. Enis
In this paper, an online Adaptive Decision Fusion (ADF) framework is proposed for the central spectrum awareness engine of a spectrum sensor network in Cognitive Radio (CR) systems. Online learning approaches are powerful tools for problems where drifts in concepts take place. Cooperative spectrum sensing in cognitive radio networks is such a problem where channel characteristics and utilization patterns change frequently. The importance of this problem stems from the requirement that secondary users must adjust their frequency utilization strategies in such a way that the communication performance of the primary users would not be degraded by any means. In the proposed framework, sensing values from several sensor nodes are fused together by weighted linear combination at the central spectrum awareness engine. The weights are updated on-line according to an active fusion method based on performing orthogonal projections onto convex sets describing power reading values from each sensor. The proposed adaptive fusion strategy for cooperative spectrum sensing can operate independent from the channel type between the primary user and secondary users. Results of simulations and experiments for the proposed method conducted in laboratory are also presented. © 2011 IEEE.
Open Access
Adaptive mixture methods based on Bregman divergences
(Elsevier, 2013) Donmez, M. A.; Inan, H. A.; Kozat, S. S.
We investigate adaptive mixture methods that linearly combine outputs of m constituent filters running in parallel to model a desired signal. We use Bregman divergences and obtain certain multiplicative updates to train the linear combination weights under an affine constraint or without any constraints. We use unnormalized relative entropy and relative entropy to define two different Bregman divergences that produce an unnormalized exponentiated gradient update and a normalized exponentiated gradient update on the mixture weights, respectively. We then carry out the mean and the mean-square transient analysis of these adaptive algorithms when they are used to combine outputs of m constituent filters. We illustrate the accuracy of our results and demonstrate the effectiveness of these updates for sparse mixture systems.
Open Access
Extraction of sparse spatial filters using Oscillating Search
(IEEE, 2012) Onaran, İbrahim; İnce, N. Fırat; Abosch, A.; Çetin, A. Enis
Common Spatial Pattern algorithm (CSP) is widely used in Brain Machine Interface (BMI) technology to extract features from dense electrode recordings by using their weighted linear combination. However, the CSP algorithm, is sensitive to variations in channel placement and can easily overfit to the data when the number of training trials is insufficient. Construction of sparse spatial projections where a small subset of channels is used in feature extraction, can increase the stability and generalization capability of the CSP method. The existing 0 norm based sub-optimal greedy channel reduction methods are either too complex such as Backward Elimination (BE) which provided best classification accuracies or have lower accuracy rates such as Recursive Weight Elimination (RWE) and Forward Selection (FS) with reduced complexity. In this paper, we apply the Oscillating Search (OS) method which fuses all these greedy search techniques to sparsify the CSP filters. We applied this new technique on EEG dataset IVa of BCI competition III. Our results indicate that the OS method provides the lowest classification error rates with low cardinality levels where the complexity of the OS is around 20 times lower than the BE. © 2012 IEEE.
Open Access
Radiation impedance of collapsed capacitive micromachined ultrasonic transducers
(Institute of Electrical and Electronics Engineers, 2012) Ozgurluk, A.; Atalar, Abdullah; Köymen, Hayrettin; Olçum, S.
The radiation impedance of a capacitive micromachined ultrasonic transducer (CMUT) array is a critical parameter to achieve high performance. In this paper, we present a calculation of the radiation impedance of collapsed, clamped, circular CMUTs both analytically and using finite element method (FEM) simulations. First, we model the radiation impedance of a single collapsed CMUT cell analytically by expressing its velocity profile as a linear combination of special functions for which the generated pressures are known. For an array of collapsed CMUT cells, the mutual impedance between the cells is also taken into account. The radiation impedances for arrays of 7, 19, 37, and 61 circular collapsed CMUT cells for different contact radii are calculated both analytically and by FEM simulations. The radiation resistance of an array reaches a plateau and maintains this level for a wide frequency range. The variation of radiation reactance with respect to frequency indicates an inductance-like behavior in the same frequency range. We find that the peak radiation resistance value is reached at higher kd values in the collapsed case as compared with the uncollapsed case, where k is the wavenumber and d is the center-to-center distance between two neighboring CMUT cells.