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Spectrum sensing, i.e., detecting the presence of primary users in a licensed spectrum, is a fundamental problem in cognitive radio. Since the statistical covariances of received signal and noise are usually different, they can be used to…

Information Theory · Computer Science 2016-09-08 Yonghong Zeng , Ying-Chang Liang

This letter explores covariance matching-based adaptive robust cubature Kalman filter (CMRACKF). In this method, the innovation sequence is used to determine the covariance matrix of measurement noise that can overcome the limitation of…

Systems and Control · Electrical Eng. & Systems 2021-06-22 Mundla Narasimhappa , Sesham Srinu

In this paper, the problem of determining the number of signal sources impinging on an array of sensors and estimating their directions-of-arrival (DOAs) in the presence of spatially white nonuniform noise is considered. It is known that,…

Signal Processing · Electrical Eng. & Systems 2021-09-10 Mahmood Karimi

Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies by finding maximally correlated embeddings of pairs of random variables. CA has found applications in fields ranging from…

Machine Learning · Computer Science 2020-07-01 Hsiang Hsu , Salman Salamatian , Flavio P. Calmon

Understanding causal relationships within a system is crucial for uncovering its underlying mechanisms. Causal discovery methods, which facilitate the construction of such models from time-series data, hold the potential to significantly…

Machine Learning · Computer Science 2024-07-31 Zsigmond Benkő , Bálint Varga , Marcell Stippinger , Zoltán Somogyvári

We consider the problem of direction-of-arrival (DOA) estimation in unknown partially correlated noise environments where the noise covariance matrix is sparse. A sparse noise covariance matrix is a common model for a sparse array of…

Information Theory · Computer Science 2016-11-15 Mohammadreza Malek-Mohammadi , Magnus Jansson , Arash Owrang , Ali Koochakzadeh , Massoud Babaie-Zadeh

Principal Component Analysis (PCA) is one of the most used tools for extracting low-dimensional representations of data, in particular for time series. Performances are known to strongly depend on the quality (amount of noise) and the…

Applications · Statistics 2024-12-16 Mariia Legenkaia , Laurent Bourdieu , Rémi Monasson

Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization of a classifier by making the classifier over-fitted to noisy labels. Existing methods on noisy label have focused on modifying the…

Machine Learning · Computer Science 2022-05-27 HeeSun Bae , Seungjae Shin , Byeonghu Na , JoonHo Jang , Kyungwoo Song , Il-Chul Moon

We propose a multiple imputation method based on principal component analysis (PCA) to deal with incomplete continuous data. To reflect the uncertainty of the parameters from one imputation to the next, we use a Bayesian treatment of the…

Methodology · Statistics 2015-08-20 Vincent Audigier , François Husson , Julie Josse

The identification of spacial noise correlation is of critical importance in developing error-corrected quantum devices, but it has barely been studied so far. In this work, we utilize an effective new method called qubit motion, to…

A core task in multi-modal learning is to integrate information from multiple feature spaces (e.g., text and audio), offering modality-invariant essential representations of data. Recent research showed that, classical tools such as {\it…

Machine Learning · Computer Science 2024-10-02 Subash Timilsina , Sagar Shrestha , Xiao Fu

Coherent information quantifies the transmittable quantum information through a channel and is directly linked to the channel's quantum capacity. In a monitored quantum circuit, regarded as a quantum channel, extensive and positive coherent…

Quantum Physics · Physics 2025-12-11 Dongheng Qian , Jing Wang

Noise remains the major obstacle to scalable quantum computation. Quantum benchmarking provides key information on noise properties and is an important step for developing more advanced quantum processors. However, current benchmarking…

Quantum Physics · Physics 2023-09-22 Yanwu Gu , Wei-Feng Zhuang , Xudan Chai , Dong E. Liu

Quantum circuits implementing fault-tolerant quantum error correction (QEC) for the three qubit bit-flip code and five-qubit code are studied. To describe the effect of noise, we apply a model based on a generalized effective Hamiltonian…

Quantum Physics · Physics 2016-09-08 Y. C. Cheng , R. J. Silbey

The Coherent Multiplex is formalized and validated as a scalable, real-time system for identifying, analyzing, and visualizing coherence among multiple time series. Its architecture comprises a fast spectral similarity layer based on cosine…

Signal Processing · Electrical Eng. & Systems 2025-08-28 Noah Shore

The ability to transfer coherent quantum information between systems is a fundamental component of quantum technologies and leads to coherent correlations within the global quantum process. However correlation structures in quantum channels…

Quantum Physics · Physics 2022-05-03 Matthew Girling , Cristina Cirstoiu , David Jennings

Chromatic calorimetry (CCAL) analyses particle detection by utilizing scintillators with distinct emission wavelengths to measure the longitudinal energy deposition of particle showers in high-energy physics, improving particle…

High Energy Physics - Experiment · Physics 2025-09-12 Devanshi Arora

The calculating of the coherent information is a fundamental step in obtaining the quantum capacity of a quantum channel. We introduce orthogonal and complete code basis to evaluate the coherent information per channel use when the input is…

Quantum Physics · Physics 2010-07-28 Xiao-yu Chen , Li-zhen Jiang

Quantum noise is ubiquitous to quantum systems as they incessantly interact with their surroundings and results in degrading useful resources such as coherence for single quantum systems and quantum correlations for multipartite systems.…

Quantum Physics · Physics 2016-11-30 Kaifeng Bu , Swati , Uttam Singh , Junde Wu

For multivariate regularly random vectors of dimension $d$, the dependence structure of the extremes is modeled by the so-called angular measure. When the dimension $d$ is high, estimating the angular measure is challenging because of its…

Methodology · Statistics 2025-05-29 Lucas Butsch , Vicky Fasen-Hartmann
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