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A generalized mathematical model is proposed for behaviors prediction of biological causal systems with multiple inputs and multiple outputs (MIMO). The system properties are represented by a set of model parameters, which can be derived with random input stimuli probing it. The system calculates predicted outputs based on the estimated parameters and its novel inputs. An efficient hardware architecture...
Channel accessibility by a secondary user (SU) in cognitive radio networks (CRNs) depends on the availability of the spectrum based on primary user and other SU activities. A new SU request may be blocked and an ongoing SU service may also be discarded if no sufficient spectrum is available. So far, little work has been done to analyze the reliability and availability aspects of CRNs from the perspective...
Multi-cell coordinated beamforming (CB) can mitigate inter-cell interference. However, previous study on CB focuses on systems with only one receive antenna. This paper considers CB for systems with multiple receive antennas. To take fairness among scheduled users into account, CB is designed to maximize the harmonic sum of signal-to-interference-plus-noise ratio (SINR). We develop an iterative algorithm...
In this paper, we propose an FPGA-based hardware architecture for conducting real-time prediction of neural activity using a second-order generalized Laguerre-Volterra model (GLVM). This architecture serves as a rapid prototype of the prediction module of the future cognitive neural prosthetic device. We validate the functionality of the hardware model by utilizing the neuronal firing data of behaving...
It is important to identify node role and track node evolution in temporal networks in many applications. Most existing methods identify the role of a node according to its static structural property. In this paper, we propose a new representation named quantitative temporal directed graph to represent temporal networks, which differs from other network representations in that it adds quantitative...
Unsupervised clustering of objects is often needed for image and video summarization, tracking and segmentation. Shape, as fundamental representation of objects, is hard to do clustering task since usual clustering algorithms need quantitative features which are very hard to extract in shapes. In this paper, we proposed a novel approach to shape clustering. To overcome the difficulty of extracting...
This paper presents a novel approach to hierarchical shape classification. We combine two shape features: contour and skeleton. Weights of two features are learned through large-margin optimization. The proposed approach uses a shape tree to efficiently represent the similarity of different shape classes. The tree is generated offline by a bottom-up clustering approach using stochastic optimization...
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