22 papers · ranked by Valyu relevance
Mohammad Reza Zamani, Valery Ugrinovskii
— The paper addresses the problem of distributed filtering with guaranteed convergence properties using minimumenergy filtering and H∞ filtering methodologies. A linear state space plant model is considered observed by a network of communicating sensors, in which individual sensor measurements may lead to an…
Raquel Caballero-Águila, Aurora Hermoso-Carazo, Josefa Linares-Pérez
In this paper, the distributed filtering problem is addressed for a class of discrete-time stochastic systems over a sensor network with a given topology, susceptible to suffering deception attacks, launched by potential adversaries, which can randomly succeed or not with a known success probability, which is not…
Alessandro Emanuele, Francesco Gasparotto, Giacomo Guerra, Mattia Zorzi
'Mattia Zorzi'] We propose a distributed Kalman filter for a sensor network under model uncertainty. The distributed scheme is characterized by two communication stages in each time step: in the first stage, the local units exchange their observations and then they can compute their local estimate; in the final stage…
Fengzeng Zhu, Xu Liu, Jiwei Wen, Linbo Xie + 1 more
This paper is concerned with the distributed full- and reduced-order $l_{2}$- $l_{\infty}$ state estimation issue for a class of discrete time-invariant systems subjected to both randomly occurring switching topologies and deception attacks over wireless sensor networks. Firstly, a switching topology model is proposed…
Jun Li, Devkishen Sisodia, Yebo Feng, Lumin Shi + 3 more
'Christopher Early' 'Peter Reiher'] Abstract—Despite the proliferation of trac ltering capabilities throughout the Internet, attackers continue to launch distributed denial-of-service (DDoS) attacks to successfully overwhelm the victims with DDoS trac. In this paper, we introduce a distributed ltering system that…
Simone Scardapane, Jie Chen, Cédric Richard
In this chapter, we analyze nonlinear filtering problems in distributed environments, e.g., sensor networks or peer-to-peer protocols. In these scenarios, the agents in the environment receive measurements in a streaming fashion, and they are required to estimate a common (nonlinear) model by alternating local…
Tadesse Ghirmai, Mianxiong Dong, Zhi Liu, Anfeng Liu + 1 more
For efficient and accurate estimation of the location of objects, a network of sensors can be used to detect and track targets in a distributed manner. In nonlinear and/or non-Gaussian dynamic models, distributed particle filtering methods are commonly applied to develop target tracking algorithms. An important…
Dahir H. Dini, Sithan Kanna, Danilo P. Mandic
—We introduce cooperative sequential state space estimation in the domain of augmented complex statistics, whereby nodes in a network collaborate locally to estimate noncircular complex signals. For rigour, a distributed augmented (widely linear) complex Kalman filter (D-ACKF) suited to the generality of complex…
Hunza Zainab, Giorgio Audrito, Soura Dasgupta, Jacob Beal
—Distributed data collection is a fundamental task in open systems. In such networks, data is aggregated across a network to produce a single aggregated result at a source device. Though self-stabilizing, algorithms performing data collection can produce large overestimates in the transient phase. For example, in [1]…
Efthalia Karydi, Konstantinos G. Margaritis
Collaborative filtering is amongst the most preferred techniques when implementing recommender systems. Recently, great interest has turned towards parallel and distributed implementations of collaborative filtering algorithms. This work is a survey of the parallel and distributed collaborative filtering…
Baishen Wei, Brett Nener
Space debris tracking is a challenge for spacecraft operation because of the increasing number of both satellites and the amount of space debris. This paper investigates space debris tracking using marginalized $δ$-generalized labeled multi-Bernoulli filtering on a network of nodes consisting of a collection of sensors…
Maxim Lippeveld, Daniel Peralta, Andrew Filby, Yvan Saeys
Due to high resolution and throughput of modern image cytometry platforms, morphologically profiling generated datasets poses a significant computational challenge. Here, we present Scalable Cytometry Image Processing (SCIP), an image processing software aimed at running on distributed high performance computing…
Denis Salopek, Miljenko Mikuc, Xiaojie Wang
The increasing network speeds of today’s Internet require high-performance, high-throughput network devices. However, the lack of affordable, flexible, and readily available devices poses a challenge for packet classification and filtering. This problem is exacerbated by the increase in volumetric Distributed…
Swier Garst, Julian Dekker, Marcel Reinders
Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be…
Bishal Thapaliya, Riyasat Ohib, Eloy Geenjar, Jingyu Liu + 2 more
Recent advancements in neuroimaging have led to greater data sharing among the scientific community. However, institutions frequently maintain control over their data, citing concerns related to research culture, privacy, and accountability. This creates a demand for innovative tools capable of analyzing amalgamated…
Althea Hansel-Harris, Andreas Tillack, Diogo Santos-Martins, Matthew Holcomb + 1 more
Virtual screening using molecular docking is now routinely used for the rapid evaluation of very large ligand libraries. As such, it has become an increasingly common approach in early-stage drug discovery. These screenings generate large amounts of data proportional to the size of the compound library used, which must…
Authors not listed
Next Generation Risk Assessment (NGRA) promotes animal-free, exposure-informed, and hypothesis-driven approaches to chemical safety assessment. In silico tools, such as quantitative structure-activity relationship (QSAR) models, are valuable new approach methodologies (NAMs) for use in NGRA. However, the practical…
Authors not listed
Protein kinases play a crucial role in key regulatory cell processes and are known to be dysregulated in diseases such as cancer and autoimmune disorders. Hence, protein kinases represent a vital drug target class. To meet the challenge of designing novel kinase inhibitors, fragment-based drug discovery (FBDD) has…
José Augusto Fontenele Magalhães, Muhammad Fuady Emzir, Francesco Corona
In order to characterise the dynamics of a biochemical system such as the chemostat, we consider a differential description of the evolution of its state under environmental fluctuations. We present solutions to the filtering problem for a chemostat subjected to geometric Brownian motion. Under this modelling…
Noah Lewis, Harshvardhan Gazula, Sergey M. Plis, Vince D. Calhoun
In this age of big data, large data stores allow researchers to compose robust models that are accurate and informative. In many cases, the data are stored in separate locations requiring data transfer between local sites, which can cause various practical hurdles, such as privacy concerns or heavy network load. This…
Lester Melie-Garcia, Bogdan Draganski, John Ashburner, Ferath Kherif
We propose a Multiple Linear Regression (MLR) methodology for the analysis of distributed and Big Data in the framework of the Medical Informatics Platform (MIP) of the Human Brain Project (HBP). MLR is a very versatile model, and is considered one of the workhorses for estimating dependences between clinical…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…