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In this paper, a network-based approach for studying the relation between the tremor intensity and the brain connectivity of Parkinson's patients is introduced. We propose an adaptive multitask diffusion strategy to estimate the underlying model between the gait information and the electroencephalography signals. Furthermore, the method incorporates an S-transform-based connectivity measure that performs...
Automatic classification of human epithelial type-2 (HEp-2) cells can improve the diagnostic process of autoimmune diseases (ADs) in terms of lower cost, faster response, and better repeatability. However, most of the proposed methods for classification of HEp-2 cells suffer from several constraints including tedious parameter tuning, massive memory requirement, and high computational costs. We propose...
The focus of this paper is on multitask learning over adaptive networks where different clusters of nodes have different objectives. We propose an adaptive regularized diffusion strategy using Gaussian kernel regularization to enable the agents to learn about the objectives of their neighbors and to ignore misleading information. In this way, the nodes will be able to meet their objectives more accurately...
In this paper we model the biological networks of bacteria and antibacterial agents and investigate the effects of cooperation in the corresponding self-organized networks. The cooperative foraging of the bacteria has been used to solve non-gradient optimization problems. In order to obtain a more realistic model of the process, we extend the previously introduced strategies for bacteria motility...
In this paper a new imaging technique for early stage ultra wideband (UWB) microwave breast cancer detection is proposed. A circular array of antennas illuminates the breast tissue with UWB pulses and the backscattered signals are then passed through a beamformer designed and applied in frequency domain. This design enables the beamformer to compensate for non-integer delays and frequency dependent...
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