Abstract: An electronic device is provided. The electronic device includes a receiving circuit configured to wirelessly receive power and output AC power, a rectifying circuit configured to rectify the AC power from the receiving circuit, wherein the rectifying circuit may include a first P-MOSFET configured to transfer a positive amplitude of power to an output terminal of the rectifying circuit while the AC power has the positive amplitude and to prevent transferring a negative amplitude of power to the output terminal of the rectifying circuit while the AC power has the negative amplitude, and a forward loss compensating circuit connected with the first P-MOSFET configured to reduce a threshold voltage of the first P-MOSFET while the AC power has the positive amplitude.

Abstract: Graph embedding is incorporated into nonnegative matrix factorization, NMF, while using the original formulation of graph embedding. Negative values are permitted in the definition of graph embedding without violating the nonnegative requirement of NMF. The factorized matrices of NMF are found by an iterative process.

Abstract: Supervised kernel nonnegative matrix factorization generates a descriptive part-based representation of data, based on the concept of kernel nonnegative matrix factorization (kernel NMF) aided by the discriminative concept of graph embedding. An iterative procedure that optimizes suggested formulation based on Pareto optimization is presented. The present formulation removes any dependence on combined optimization schemes.

Abstract: Supervised nonnegative matrix factorization (SNMF) generates a descriptive part-based representation of data, based on the concept of nonnegative matrix factorization (NMF) aided by the discriminative concept of graph embedding. An iterative procedure that optimizes suggested formulation based on Pareto optimization is presented. The present formulation removes any dependence on combined optimization schemes. Analytical and empirical evidence is presented to show that SNMF has advantages over popular subspace learning techniques as well as current state-of-the-art techniques.

Abstract: Graph embedding is incorporated into nonnegative matrix factorization, NMF, while using the original formulation of graph embedding. Negative values are permitted in the definition of graph embedding without violating the nonnegative requirement of NMF. The factorized matrices of NMF are found by an iterative process.

Abstract: Supervised kernel nonnegative matrix factorization generates a descriptive part-based representation of data, based on the concept of kernel nonnegative matrix factorization (kernel NMF) aided by the discriminative concept of graph embedding. An iterative procedure that optimizes suggested formulation based on Pareto optimization is presented. The present formulation removes any dependence on combined optimization schemes.

Abstract: Supervised nonnegative matrix factorization (SNMF) generates a descriptive part-based representation of data, based on the concept of nonnegative matrix factorization (NMF) aided by the discriminative concept of graph embedding. An iterative procedure that optimizes suggested formulation based on Pareto optimization is presented. The present formulation removes any dependence on combined optimization schemes. Analytical and empirical evidence is presented to show that SNMF has advantages over popular subspace learning techniques as well as current state-of-the-art techniques.