294 research outputs found
Factor Graph Processing for Dual-Blind Deconvolution at ISAC Receiver
Integrated sensing and communications (ISAC) systems have gained significant
interest because of their ability to jointly and efficiently access, utilize,
and manage the scarce electromagnetic spectrum. The co-existence approach
toward ISAC focuses on the receiver processing of overlaid radar and
communications signals coming from independent transmitters. A specific ISAC
coexistence problem is dual-blind deconvolution (DBD), wherein the transmit
signals and channels of both radar and communications are unknown to the
receiver. Prior DBD works ignore the evolution of the signal model over time.
In this work, we consider a dynamic DBD scenario using a linear state space
model (LSSM) such that, apart from the transmit signals and channels of both
systems, the LSSM parameters are also unknown. We employ a factor graph
representation to model these unknown variables. We avoid the conventional
matrix inversion approach to estimate the unknown variables by using an
efficient expectation-maximization algorithm, where each iteration employs a
Gaussian message passing over the factor graph structure. Numerical experiments
demonstrate the accurate estimation of radar and communications channels,
including in the presence of noise.Comment: 13 pages, 4 figure
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
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Advances in Statistical Machine Learning Methods for Neural Data Science
Innovations in neural data recording techniques are revolutionizing neuroscience and presenting both challenges and opportunities for statistical data analysis. This dissertation discusses several recent advances in neural data signal processing, encoding, decoding, and dimension reduction. Chapter 1 introduces challenges in neural data science and common statistical methods used to address them. Chapter 2 develops a new method to detect neurons and extract signals from noisy calcium imaging data with irregular neuron shapes. Chapter 3 introduces a novel probabilistic framework for modeling deconvolved calcium traces. Chapter 4 proposes an improved Bayesian nonparametric extension of the hidden Markov model (HMM) that separates the strength of the self-persistence prior and transition prior. Chapter 5 introduces a more identifiable and interpretable latent variable model for Poisson observations. We develop efficient algorithms to fit each of the aforementioned methods and demonstrate their effectiveness on both simulated and real data
Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis
Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem
Analog Photonics Computing for Information Processing, Inference and Optimisation
This review presents an overview of the current state-of-the-art in photonics
computing, which leverages photons, photons coupled with matter, and
optics-related technologies for effective and efficient computational purposes.
It covers the history and development of photonics computing and modern
analogue computing platforms and architectures, focusing on optimization tasks
and neural network implementations. The authors examine special-purpose
optimizers, mathematical descriptions of photonics optimizers, and their
various interconnections. Disparate applications are discussed, including
direct encoding, logistics, finance, phase retrieval, machine learning, neural
networks, probabilistic graphical models, and image processing, among many
others. The main directions of technological advancement and associated
challenges in photonics computing are explored, along with an assessment of its
efficiency. Finally, the paper discusses prospects and the field of optical
quantum computing, providing insights into the potential applications of this
technology.Comment: Invited submission by Journal of Advanced Quantum Technologies;
accepted version 5/06/202
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