A method is presented for obtaining the error probability for block codes. The method is based on the eigenvalueeigenvector properties of the code correlation matrix. It is found that under a unary transformation and for an additive white Gaussian noise environment, the performance evaluation of a block code becomes a one-dimensional problem in which only one eigenvalue and its corresponding ei...

This paper proposes novel end-to-end framework for detecting suspicious pulmonary nodules in chest CT scans. The method’s core idea is a new nodule segmentation architecture with model-based feature projection block on three-dimensional convolutions. acts as preliminary extractor two-dimensional U-Net-like convolutional network. Using the proposed approach along an axial, coronal, and sagittal ...

We present a finite-size scaling analysis of the entanglement in a two-dimensional arrays of quantum dots modeled by the Hubbard Hamiltonian on a triangular lattice. Using multistage block renormalization group approach, we have found that there is an abrupt jump of the entanglement when a first-order quantum phase transition occurs. At the critical point, the entanglement is constant, independ...

An astonishing difference in the performance of non-orthogonal space–time block codes (NOSTBCs) on wireless multiple-input= multiple-output (MIMO) systems compared to one-dimensional block codes operating on single-input=single-output (SISO) channels is derived. Using NOSTBCs, single symbol errors are no longer the only dominating error types, but multiple symbol errors can dominate the error p...

2-dimensional (2D) block spread CDMA achieves multiple-access interference (MAI) free uplink transmission while using low-complexity single-user detection. An important problem is how efficiently to assign the limited resource of spreading codes to users. In this paper, we propose a code assignment algorithm for the uplink of a 2D block spread CDMA cellular system. It is confirmed by the comput...

We propose an accelerated stochastic block coordinate descent algorithm for nonconvex optimization under sparsity constraint in the high dimensional regime. The core of our algorithm is leveraging both stochastic partial gradient and full partial gradient restricted to each coordinate block to accelerate the convergence. We prove that the algorithm converges to the unknown true parameter at a l...