Search results for "clustering"

V11. Functional hierarchy within an overall network for visual motion processing and ocular-motor control at rest

2015

Introduction Visual motion processing on one hand and ocular motor functions on the other are rarely studied together in vivo in humans. The interrelation of these functional networks is rather unclear, even though their functional dependence seems obvious. In several fMRI studies the essential nodes of both networks could be localized using voluntary optokinetic ('look') nystagmus (OKN) in the horizontal plane incorporating visual motion tracking (Dieterich et al., 2009). Here, functional connectivity (FC) between these nodes representing both networks was studies using resting-state FC. Methods Resting-state fMRI data of 200 healthy adults (age 44.1±17.9; 79 male) were included in the cro…

Algorithms for Graph and Network Analysis: Clustering and Search of Motifs in Graphs

2019

In this article we deal with problems that involve the analysis of topology in graphs modeling biological networks. In particular, we consider two important problems: (i) Network clustering, aiming at finding compact subgraphs inside the input graph in order to isolate molecular complexes, and (ii) searching for motifs, i.e., sub-structures repeated in the input network and presenting high significance (e.g., in terms of their frequency). We provide a compact overview of the main techniques proposed in the literature to solve these problems.

Compaction of Open-Graded HMAs Evaluated by a Fuzzy Clustering Technique

2015

The aim of this paper is the proposal of an expeditious procedure to be used during the execution of an asphalt layer for improving the compaction task. This procedure, based on a fuzzy clustering technique, starts from the knowledge of some information recorded by ordinary measuring instruments and provides an aid to the decision-maker on the number of roller passes needed to achieve a specific density at a certain temperature. This result can be deduced with great rapidity during the paving operations on site without waiting for the time spent in the core extraction and in the subsequent laboratory analysis. In this way it is possible to identify more precisely which aspects of the execut…

Complex Networked Systems: Convergence Analysis, Dynamic Behaviour, and Security.

Complex networked systems are a modern reference framework through which very dierent systems from far disciplines, such as biology, computer science, physics, social science, and engineering, can be described. They arise in the great majority of modern technological applications. Examples of real complex networked systems include embedded systems, biological networks, large-scale systems such as power generation grids, transportation networks, water distribution systems, and social network. In the recent years, scientists and engineers have developed a variety of techniques, approaches, and models to better understand and predict the behaviour of these systems, even though several research…

Cluster-based RF fingerprint positioning using LTE and WLAN signal strengths

2017

Wireless Local Area Network (WLAN) positioning has become a popular localization system due to its low-cost installation and widespread availability of WLAN access points. Traditional grid-based radio frequency (RF) fingerprinting (GRFF) suffers from two drawbacks. First it requires costly and non-efficient data collection and updating procedure; secondly the method goes through time-consuming data pre-processing before it outputs user position. This paper proposes Cluster-based RF Fingerprinting (CRFF) to overcome these limitations by using modified Minimization of Drive Tests data which can be autonomously collected by cellular operators from their subscribers. The effect of environmental…

Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

2022

In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lo…

Complex networks : application for texture characterization and classification

2008

This article describes a new method and approch of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we presente how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditionnal and extended hierarchical measurements, are used to characterize ”organisation” of textures.

SCCF Parameter and Similarity Measure Optimization and Evaluation

2019

Neighborhood-based Collaborative Filtering (CF) is one of the most successful and widely used recommendation approaches; however, it suffers from major flaws especially under sparse environments. Traditional similarity measures used by neighborhood-based CF to find similar users or items are not suitable in sparse datasets. Sparse Subspace Clustering and common liking rate in CF (SCCF), a recently published research, proposed a tunable similarity measure oriented towards sparse datasets; however, its performance can be maximized and requires further analysis and investigation. In this paper, we propose and evaluate the performance of a new tuning mechanism, using the Mean Absolute Error (MA…

Automated prostate gland segmentation based on an unsupervised fuzzy C-means clustering technique using multispectral T1w and T2w MR imaging

2017

Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T…

Efficient and Accurate OTU Clustering with GPU-Based Sequence Alignment and Dynamic Dendrogram Cutting.

2015

De novo clustering is a popular technique to perform taxonomic profiling of a microbial community by grouping 16S rRNA amplicon reads into operational taxonomic units (OTUs). In this work, we introduce a new dendrogram-based OTU clustering pipeline called CRiSPy. The key idea used in CRiSPy to improve clustering accuracy is the application of an anomaly detection technique to obtain a dynamic distance cutoff instead of using the de facto value of 97 percent sequence similarity as in most existing OTU clustering pipelines. This technique works by detecting an abrupt change in the merging heights of a dendrogram. To produce the output dendrograms, CRiSPy employs the OTU hierarchical clusterin…