Dictionary learning plays an important role in machine learning, where data vectors are modeled as a sparse linear combinations of basis factors (i.e., dictionary). However, how to conduct dictionary learning in noisy environment has not been well studied. Moreover, in practice, the dictionary (i.e., the lower rank approximation of the data matrix) and the sparse representations are required to be nonnegative, such as applications for image annotation, document summarization, microarray analysis. In this paper, we propose a new formulation for non-negative dictionary learning in noisy environment, where structure sparsity is enforced on sparse representation. The proposed new formulation is also robust for data with noises and outliers, due to a robust loss function used. We derive an efficient multiplicative updating algorithm to solve the optimization problem, where dictionary and sparse representation are updated iteratively. We prove the convergence and correctness of proposed algorithm rigorously.We show the differences of dictionary at different level of sparsity constraint.The proposed algorithm can be adapted for clustering and semi-supervised learning.
Radio frequency identification (RFID) tags are small electronic devices working in the radio frequency range. They use wireless radio communications to automatically identify objects or people without the need for line-of-sight or contact, and are widely used in inventory tracking, object location, environmental monitoring. This paper presents a design of a covert RFID tag network for target discovery and target information routing. In the design, a static or very slowly moving target in the field of RFID tags transmits a distinct pseudo-noise signal, and the RFID tags in the network collect the target information and route it to the command center. A map of each RFID tag’s location is saved at command center, which can determine where a RFID tag is located based on each RFID tag’s ID. We propose the target information collection method with target association and clustering, and we also propose the information routing algorithm within the RFID tag network. The design and operation of the proposed algorithms are illustrated through examples. Simulation results demonstrate the effectiveness of the design.
Radio frequency (RF) tags have been widely used in inventory tracking, environmental monitoring, battlefield situational awareness, and combat identification due to their low cost, small size, and wireless functionality. This paper explores the application of active RF tags in outdoor environments responding to random noise radar interrogations with predetermined messages. A conceptual system design for communication between radar and RF tags using ultrawideband (UWB) noise waveforms is proposed and analyzed theoretically and via simulations. The tag structure comprises a sensing receiver and active receiver/transmitter. The sensing receiver senses the radar header consisting of a prearranged secret realization of the noise waveform. The active receiver/transmitter modulates the RF tag's message onto the radar interrogation signal through weighted tapped delays and reradiates the tag message back to the radar. System performance is evaluated in terms of symbol error probability in an additive white Gaussian noise (AWGN) channel. A technique to combat multipath interference is presented. It is shown that this system is capable of communicating a suite of messages from the tags to the radar.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.