Signal processing is a concept that focuses on analyzing, modifying, and synthesizing signals such as sound, seismic data, and altimeter measurements. The fundamental challenges in signal processing ...

The easiest way to start with tabmat is to use the convenience constructor tabmat.from_pandas. TL;DR: We provide matrix classes for efficiently building statistical algorithms with data that is ...

Transforms quantum states into the Fourier space, making it easier to extract periods and phases hidden in the amplitudes. Purpose: An operation that transforms quantum states while maintaining the ...

Abstract: The simultaneous orthogonal matching pursuit (SOMP) algorithm and its variants are the main recovery algorithms based on sub-Nyquist sampling (SNS) in Wideband spectrum sensing (WBSS). In ...

Abstract: In this paper, authentication for mobile radio frequency identification (RFID) systems with low-cost tags is investigated. To this end, an adaptive modulus (AM) encryption algorithm is first ...

To handle principal component analysis (PCA)-based missing data with high correlation, we propose a novel imputation algorithm to impute missing values, called iterated score regression. The procedure ...

NonnegMFPy ----- NonnegMFPy is developed and maintained by Guangtun Ben Zhu, It is designed to solve nonnegative matrix factorization (NMF) given a dataset with heteroscedastic uncertainties and ...

Current motor imagery-based brain-computer interface (BCI) systems require a long calibration time at the beginning of each session before they can be used with adequate levels of classification ...

The Locally Competitive Algorithm (LCA) is a biologically plausible computational architecture for sparse coding, where a signal is represented as a linear combination of elements from an ...

Large-scale Machine Learning (ML) algorithms are often iterative, using repeated read-only data access and I/O-bound matrix-vector multiplications. Hence, it is crucial for performance to fit the data ...