Search

We develop an automatic method for synaptic partner identification in insect brains and use it to predict synaptic partners in a whole-brain electron microscopy dataset of the fruit fly. The predictions can be used to infer a connectivity graph with high accuracy, thus allowing fast identification of neural pathways. To facilitate circuit reconstruction using our results, we develop CIRCUITMAP, a user interface add-on for the circuit annotation tool CATMAID. A deep-learning-based approach enables automatic identification of synaptically connected neurons in electron microscopy datasets of the fly brain.

Gossip Learning (GL) is a fully decentralized machine learning paradigm with the potential to enable highly scalability and to preserve user privacy. The majority of existing results however consider scenarios in which either each node communicates with all other nodes, or in which the connectivity graph is static, and they are therefore inapplicable in dynamic setups such as in VANETs. This work is a first attempt at designing and assessing GL schemes suited for scenarios with moving nodes with the application of predicting the trajectory of moving cars.

In a multi-agent system, the presence of agents that intentionally share misleading information or have suffered a failure (non-cooperative agents) affects its normal operation. Specific characteristics of the connectivity graph of the system can help to avoid this situation. Here, a resilience algorithm for multi-agent systems is studied and applied so that the system is able to reach its global task. Specifically, the conditions on the connectivity graph, update rule and bounds on the deviation of shared information among the agents are studied so that a resilient consensus is achieved. Both, the algorithm and the analysis is applied in a group of heterogeneous robots whose global task is formation and trajectory tracking. Further, after the identification of non-cooperative agents, the re-configuration of the agents is such that the robustness of the graph is kept.

Emerging data-driven modeling methods are affording new opportunities for building bio-inspired models for neurosensory integration. Of particular interest is understanding how neural pathways from input stimuli to motor-neuron driven behavioral responses function. Importantly, data-driven models are capable of framing neurosensory integration into a modern control theoretic framework, allowing for an improved understanding of the role of network structure and function. Two model organisms are considered here: Caenorhabditis elegans (C. elegans) and (ii) the Manduca Sexta (Manduca) moth. These well studied model organisms exploit different network architectures for functionality, with the former leveraging a small, stereotyped connectivity graph for its function, while the latter makes use of a large, randomly connected network for processing sensory (olfactory) information and learning. These two alternative strategies give insight into the diversity of neurosensory strategies available to organisms for producing robust and stable behaviors in a noisy stimulus environment.

Graph network analysis (GNA) showed a remarkable role for understanding brain functions, but its application is mainly nar-rowed to fMRI research. Connectivity analysis (CA) is introduced as a signal-to-graph mapping in a time-causality framework. In this paper, we investigate the application of GNA/CA in fNIRS. To solve the inher-ent challenges of using CA, we also propose a novel metric: a maximum cross-lag magnitude (MCLM) that eÿciently extracts major causality in-formation. We tested MCLM in four types of cognitive activities (mental arithmetic, motor imagery, word generation, and brain workload) from 55 participants. CA/MCLM showed a compelling modeling capacity and re-vealed unexpected cross-subject network patterns. We found that motion imagery and mental arithmetic share a background network structure, and that the right prefrontal cortex, in AFp8, is an invariable destina-tion for information flows in every stimuli and participant. Therefore, CA/MCLM-fNIRS showed potential for its use along with fNIRS in clin-ical studies. This work is financially supported by the Research Council of Norway to the Project No. 273599, "Patient-Centric Engineering in Rehabilitation (PACER)".

Given high-dimensional, graph-smooth and grossly-corrupted signals, this paper presents the algorithm to simultaneously estimate the intrinsic low-rank components and the underlying graph from high-dimensional noisy graph signal. We assume that the perturbation on low-rank components is sparse and signals are smooth on unknown underlying graph. The proposed algorithm learns the low-rank components by exploiting estimate of the graph, and refines the graph estimation with learned low-rank components. We propose two solutions to this problem: One applies alternating optimization to solve the subproblems. The other solves the problem directly. Furthermore, we analyze the impact of inexact graph on low-rank components estimation to justify our approach. We conduct extensive experiments on the proposed algorithm with synthetic data and real brain imaging data, Magnetoencephalography (MEG) and compare it with state-of-the-art methods. In particular, our algorithm is applied to estimate the low-rank components for classifying MEG signals evoked when a subject views a face or non-face image. We observe that our proposed algorithm is competitive in estimating low-rank components, adequately capturing intrinsic task-related information in lower-dimensional representation. This leads to improved classification performance. Furthermore, we notice that our estimated graph indicates brain active regions for the visual activity that are consistent with neuroscientific findings.

Microstructural geometry plays a critical role in the response of heterogeneous materials. Consequently, methods for generating microstructural samples are increasingly crucial to advanced numerical analyses. We extend Sonon et al.'s unified framework, developed originally for generating particulate and foam-like microstructural geometries of Periodic Unit Cells, to non-periodic microstructural representations based on the formalism of Wang tiles. This formalism has been recently proposed in order to generalize the Periodic Unit Cell approach, enabling a fast synthesis of arbitrarily large, stochastic microstructural samples from a handful of domains with predefined compatibility constraints. However, a robust procedure capable of designing complex, three-dimensional, foam-like and cellular morphologies of Wang tiles has not yet been proposed. This contribution fills the gap by significantly broadening the applicability of the tiling concept. Since the original Sonon et al.'s framework builds on a random sequential addition of particles enhanced with an implicit representation of particle boundaries by the level-set field, we first devise an analysis based on a connectivity graph of a tile set, resolving the question where a particle should be copied when it intersects a tile boundary. Next, we introduce several modifications to the original algorithm that are necessary to ensure microstructural compatibility in the generalized periodicity setting of Wang tiles. Having established a universal procedure for generating tile morphologies, we compare strictly aperiodic and stochastic sets with the same cardinality in terms of reducing the artificial periodicity in reconstructed microstructural samples. We demonstrate the superiority of the vertex-defined tile sets for two-dimensional problems and illustrate the capabilities of the algorithm with two- and three-dimensional examples., 16 pages, 16 figures. Abstract has been shortened to match the arXiv.org's limit of 1920 characters

Hyperspectral image (HSI) classification plays an important role in the automatic interpretation of the remotely sensed data. However, it is a non-trivial task to classify HSI accurately and rapidly due to its characteristics of having a large amount of data and massive noise points. To address this problem, in this work, a novel, semi-supervised, superpixel-level classification method for an HSI was proposed based on a graph and discrete potential (SSC-GDP). The key idea of the proposed scheme is the construction of the weighted connectivity graph and the division of the weighted graph. Based on the superpixel segmentation, a weighted connectivity graph is constructed usingthe weighted connection between a superpixel and its spatial neighbors. The generated graph is then divided into different communities/sub-graphs by using a discrete potential and the improved semi-supervised Wu–Huberman (ISWH) algorithm. Each community in the weighted connectivity graph represents a class in the HSI. The local connection strategy, together with the linear complexity of the ISWH algorithm, ensures the fast implementation of the suggested SSC-GDP method. To prove the effectiveness of the proposed spectral–spatial method, two public benchmarks, Indian Pines and Salinas, were utilized to test the performance of our proposal. The comparative test results confirmed that the proposed method was superior to several other state-of-the-art methods.

Objective An epileptic seizure has a characteristic EEG pattern, which allows for its automatic detection. Statistical dependence between different brain regions measures by functional brain connectivity (FBC). Specific directional effects cannot be considered by FBC, and thus effective brain connectivity (EBC) is used to measure causal dependencies between brain regions. Our main purpose is to provide a reliable automatic seizure detection approach. Methods In this study, three new methods are provided. Multi-level modular network (MLMN) is proposed based on combining various EBC classification results at different frequencies. Another method named “modular effective neural networks (MENN).” This method combines the classification results of the three different EBCs at a specific frequency. “Modular frequency neural networks (MFNN)” is another method that combines the classification results of specific EBC at seven different frequencies. Results The mean accuracies of MFNN are 97.14 %, 98.53 %, and 97.91 % using directed transfer function (DTF), directed coherence (DC), and generalized partial directed coherence (GPDC), respectively. By using MENN method, the highest mean accuracy is 98.34 %. Finally, MLMN has the highest mean accuracy, which is equal to 99.43. To the best of our knowledge, the proposed method is a new method that provides the highest accuracy in comparison to other studies – that used the MIT-CHB database. Conclusion and significance The knowledge of structure-function relationships between different areas of the brain is necessary for characterizing the underlying dynamics. Hence, features based on EBC can provide a more reliable automatic seizure detection approach.

We investigate a variant of the Hegselmann-Krause model of opinion dynamics that relaxes the assumption that every agent has knowledge of every other agent's opinion at all points in time. This is done by incorporating a physical connectivity graph that accounts for the external factors that may prevent interaction between certain pairs of agents. As opposed to the original Hegselmann-Krause dynamics (which terminate in finite time), we show that for any underlying graph that is connected but not complete, there exists an initial condition under which the dynamics never terminate. As a result, we show that for any continuous probability density function having the state space as its support, the expected termination time of the modified dynamics is infinity. We also study the rate of convergence to the steady state and derive bounds on the maximum convergence time in terms of the properties of the physical connectivity graph.

Quantum annealing has shown significant potential as an approach to near-term quantum computing. Despite promising progress towards obtaining a quantum speedup, quantum annealers are limited by the need to embed problem instances within the (often highly restricted) connectivity graph of the annealer. This embedding can be costly to perform and may destroy any computational speedup. Here we present a hybrid quantum-classical paradigm to help mitigate this limitation, and show how a raw speedup that is negated by the embedding time can nonetheless be exploited in certain circumstances. We illustrate this approach with initial results on a proof-of-concept implementation of an algorithm for the dynamically weighted maximum independent set problem., In Proceedings PC 2018, arXiv:1807.10563

Although many methods of spatial analysis have been developed for a better understanding and modelling of urban space analysis, there is still a need for exploration of new analytical techniques for modelling urban spaces. Space Syntax models the spatial configurations of urban spaces by using a connectivity graph representation. Such a configuration of space identifies patterns that can be used to study urban structures and human behaviors. This paper tries to present a new methodology to investigate the urban spatial structure by using Space Syntax with the GIS information including land use, buildings’ characteristics and practical evaluations of the potential of the Space Syntax approach with GIS and multivariate analysis technique. Finally we try to offer some recommendations that attempt to improve the identified problems faced in Kitakyushu, Japan.

This work was aimed at reconstructing the structural model of as-built industrial facilities like plants purely from on-site point cloud measurement data. Focus was set on finding the internal structure of complex objects hidden behind the massive point cloud by exploiting connectivity information in the data and the linear characteristics of the typical components. A workflow is presented with emphasis on data filtering, connectivity graph construction, as well as the recognition of elementary objects and their relations. Results are demonstrated using data of an industrial case study.

An approach to the formation of operational visibility and ecological risks scene displaying in geographic information systems (GIS) of environmental protection is offered. The approach is based on structural-symbolic method of presenting the basic graphic elements (BGE) (models) of ecological risks, the linguistic rules and grammar construction of BGE on the basis of a given set of contours, BGE connectivity graph and set-theoretic operations of ecological risk maps synthesis using GIS. The main methods of structural electronic BGE library construction described in the article: direct structural-symbolic method of ecological risks graphical models representing; linguistic method of representing the alphabet and ecological risk models; program and program-linguistic method for representing the alphabet of basic graphic models of ecological risks.

Over the past few years Personal Navigation Systems have become an established tool for route planning, but they are mainly designed for outdoor environments. Indoor navigation is still a challenging research area for several reasons: positioning is not very accurate, users can freely move between the interior boundaries of buildings, path network construction process may not be easy and straightforward due to complexity of indoor space configurations. Therefore the creation of a good network is essential for deriving overall connectivity of a building and for representing position of objects within the environment. This paper reviews current approaches to automatic derivation of route graphs for indoor navigation and discusses some of their limitations. Then, it introduces a novel algorithmic strategy for extracting a 3D connectivity graph for indoor navigation based on 2D floor plans.

Over-segmentation, or super-pixel generation, is a common preliminary stage for many computer vision applications. New acquisition technologies enable the capturing of 3D point clouds that contain color and geometrical information. This 3D information introduces a new conceptual change that can be utilized to improve the results of over-segmentation, which uses mainly color information, and to generate clusters of points we call super-points. We consider a variety of possible 3D extensions of the Local Variation (LV) graph based over-segmentation algorithms, and compare them thoroughly. We consider different alternatives for constructing the connectivity graph, for assigning the edge weights, and for defining the merge criterion, which must now account for the geometric information and not only color. Following this evaluation, we derive a new generic algorithm for over-segmentation of 3D point clouds. We call this new algorithm Point Cloud Local Variation (PCLV). The advantages of the new over-segmentation algorithm are demonstrated on both outdoor and cluttered indoor scenes. Performance analysis of the proposed approach compared to state-of-the-art 2D and 3D over-segmentation algorithms shows significant improvement according to the common performance measures.

This paper proposes mapping and path planning algorithms for an unknown environment using a communication graph of an unlocalized robotic swarm system. Unlike the previous efforts that used the absolute position information of all the robots, in the proposed algorithms, the unknown environment is described as the communication connectivity graph achieved via a distributed consensus algorithm. Thus, since robots have no positioning sensors, the proposed mapping and path planning algorithms can simplify the procedures and reduce the costs. The numerical simulations are included to demonstrate the performance of the proposed algorithms.

Identifying the relations among different regions of the brain is vital for a better understanding of how the brain functions. While a large number of studies have investigated the neuroanatomical and neurochemical connections among brain structures, their specific findings are found in publications scattered over a large number of years and different types of publications. Text mining techniques have provided the means to extract specific types of information from a large number of publications with the aim of presenting a larger, if not necessarily an exhaustive picture. By using natural language processing techniques, the present paper aims to identify connectivity relations among brain regions in general and relations relevant to the paraventricular nucleus of the thalamus (PVT) in particular. We introduce a linguistically motivated approach based on patterns defined over the constituency and dependency parse trees of sentences. Besides the presence of a relation between a pair of brain regions, the proposed method also identifies the directionality of the relation, which enables the creation and analysis of a directional brain region connectivity graph. The approach is evaluated over the manually annotated data sets of the WhiteText Project. In addition, as a case study, the method is applied to extract and analyze the connectivity graph of PVT, which is an important brain region that is considered to influence many functions ranging from arousal, motivation, and drug-seeking behavior to attention. The results of the PVT connectivity graph show that PVT may be a new target of research in mood assessment.

Siting a linear facility such as a highway or a pipeline often requires a preliminary study in which one or several corridors are identified. Here we construct corridors as a collection of adjacent polygons specifying a ‘path’ from origin s to destination t . Formally, we make use of a graph, called the connectivity graph, in which vertices correspond to polygons and edges to adjacent polygons. Within this formal representation, a corridor corresponds to an s to t path in the connectivity graph. The corridors are evaluated on two criteria: (1) a quantitative criterion measuring the length of the corridor, and (2) a qualitative criterion measuring the quality of the corridor with respect to the suitability of crossing its component polygons. We first introduce a three-phase approach based on a coupling between a geographical information system (GIS) and multicriteria evaluation and devoted to handling biobjective corridor siting problems. Then, the proposed approach is validated through an example of a real-world application. Keywords: GIS, corridor siting, multicriteria evaluation, spatial analysis, multiobjective shortest path

There is growing interest in studying dynamic graphs, or graphs that evolve with time. In this work, we investigate a new type of dynamic graph analysis - finding regions of a graph that are evolving in a similar manner and are topologically similar over a period of time. For example, these regions can be used to group a set of changes having a common cause in event detection and fault diagnosis. Prior work [6] has proposed a greedy framework called cSTAG to find these regions. It was accurate in datasets where the regions are temporally and spatially well separated. However, in cases where the regions are not well separated, cSTAG produces incorrect groupings. In this paper, we propose a new algorithm called regHunter. It treats the region discovery problem as a multi-objective optimisation problem, and it uses a multi-level graph partitioning algorithm to discover the regions of correlated change. In addition, we propose an external clustering validation technique, and use several existing internal measures to evaluate the accuracy of regHunter. Using synthetic datasets, we found regHunter is significantly more accurate than cSTAG in dynamic graphs that have regions with small separation. Using two real datasets - the access graph of the 1998 World Cup website, and the BGP connectivity graph during the landfall of Hurricane Katrina - we found regHunter obtained more accurate results than cSTAG. Furthermore, regHunter was able to discover two interesting regions for the World Cup access graph that CSTAG was not able to find.

In this paper, we developed a model-based and a data-driven estimator for directed information (DI) to infer the causal connectivity graph between electrocorticographic (ECoG) signals recorded from brain and to identify the seizure onset zone (SOZ) in epileptic patients. Directed information, an information theoretic quantity, is a general metric to infer causal connectivity between time-series and is not restricted to a particular class of models unlike the popular metrics based on Granger causality or transfer entropy. The proposed estimators are shown to be almost surely convergent. Causal connectivity between ECoG electrodes in five epileptic patients is inferred using the proposed DI estimators, after validating their performance on simulated data. We then proposed a model-based and a data-driven SOZ identification algorithm to identify SOZ from the causal connectivity inferred using model-based and data-driven DI estimators respectively. The data-driven SOZ identification outperforms the model-based SOZ identification algorithm when benchmarked against visual analysis by neurologist, the current clinical gold standard. The causal connectivity analysis presented here is the first step towards developing novel non-surgical treatments for epilepsy., This paper is accepted for publication in IEEE Journal of Selected Topics in Signal Processing, special issue on Advanced Signal Processing in Brain Networks, October 2016. 16 pages, 11 figures and 2 tables

We formulate the delay-tolerant networking routing problem, where messages are to be moved end-to-end across a connectivity graph that is time-varying but whose dynamics may be known in advance. The problem has the added constraints of finite buffers at each node and the general property that no contemporaneous end-to-end path may ever exist. This situation limits the applicability of traditional routing approaches that tend to treat outages as failures and seek to find an existing end-to-end path. We propose a framework for evaluating routing algorithms in such environments. We then develop several algorithms and use simulations to compare their performance with respect to the amount of knowledge they require about network topology. We find that, as expected, the algorithms using the least knowledge tend to perform poorly. We also find that with limited additional knowledge, far less than complete global knowledge, efficient algorithms can be constructed for routing in such environments. To the best of our knowledge this is the first such investigation of routing issues in DTNs.

We present a technique for calculating free-energy profiles for the nucleation of multicomponent structuresthat contain as many species as building blocks. We find that a key factor is the topology of the graphdescribing the connectivity of the target assembly. By considering the designed interactions separately fromweaker, incidental interactions, our approach yields predictions for the equilibrium yield and nucleation bar-riers. These predictions are in good agreement with corresponding Monte Carlo simulations. We show that afew fundamental properties of the connectivity graph determine the most prominent features of the assemblythermodynamics. Surprisingly, we find that polydispersity in the strengths of the designed interactions stabi-lizes intermediate structures and can be used to sculpt the free-energy landscape for self-assembly. Finally, wedemonstrate that weak incidental interactions can preclude assembly at equilibrium due to the combinatorialpossibilities for incorrect association.Building nanostructures out of multiple, distinct com-ponents offers enormous possibilities for high-fidelitymanufacturing at the molecular level. Such ‘addressable’structures are fundamentally different from conventionalcrystals or ordered clusters, since every building block isdistinct and thus occupies a specific location in the tar-get structure. Because the interactions between buildingblocks are specified independently, it is possible to designfinite-sized, three-dimensional structures that assemblenearly error-free.

Much effort has been directed towards using mathematical models to understand and predict contagious disease, in particular Ebola outbreaks. Classical SIR (susceptible-infected-recovered) compartmental models capture well the dynamics of the outbreak in certain communities, and accurately describe the differences between them based on a variety of parameters. However, repeated resurgence of Ebola contagions suggests that there are components of the global disease dynamics that we don't yet fully understand and can't effectively control. In order to understand the dynamics of a more widespread contagion, we placed SIR models within the framework of dynamic networks, with the communities at risk of contracting the virus acting as nonlinear systems, coupled based on a connectivity graph. We study how the effects of the disease (measured as the outbreak impact and duration) change with respect to local parameters, but also with changes in both short-range and long-range connectivity patterns in the graph. We discuss the implications of optimizing both these measures in increasingly realistic models of coupled communities., Comment: 16 pages, 8 figures

Electric stimulation in neural cultures in neural cultures may be used for creating adjacent physical or logical connections in the connectivity graph following Hebb’s Law modifying the neural responses principal parameters. The created biological structure may be used for computing a certain function, however this achieved structure vanished with time as the stimulation stops. A DTCNN architecture, specifically designed for optimum parallel implementation over dedicated hardware, is proposed to emulate the behavior ans structure of the biological neuronal culture. The FPGA circuit can be used as a permanent model and is also intended to facilitate and speed up further experimentation.

This paper presents an automated tool for estimating assembly times of products based on a three step process: connectivity graph generation from assembly mate information, structural complexity metric analysis of the graph, and application of the complexity metric vector to predictive artificial neural network models. The tool has been evaluated against different training set cases, suggesting that partially defined assembly models and training product variety are critical characteristics. Moreover, the tool is shown to be robust and insensitive to different modeling engineers. The tool has been implemented in a commercial CAD system and shown to yield results of within ±25% of predicted values. Additional extensions and experiments are recommended to improve the tool.

This material contains the description and the links where to find the datasets used in the paper Assessing cross-cut shredded document assembly Abstract In this paper we address the problem of quantitative evaluation of cross-cut shredded document reconstruction. We propose quantitative metrics using graph theory and classic information retrieval concepts to compare the neighborhood connectivity graph of a reassembled document shredded by a cross-cut machine against the neighborhood graph of the ground-truth. These metrics focus entirely on the proper relative positioning of the shredded pieces. To do so, we have shredded 12 documents containing diverse content, such as handwriting, printed text, images and photographs. We then scanned, extracted the pieces, and reassembled them into the ground-truth. This dataset is available to the readers, with the original documents, the digital representation of the shreds, and the scripts that provide the quantitative evaluation of the user’s reconstructions.

We have implemented a multiscale vision model based on the wavelet transform to analyse field astronomical images. The discrete transform is performed by the a trous algorithm. The vision model is based on the notion of significant structures. We identify the pixels of the associated wavelet transform space (WTS) with the objects. For each scale a region labelling is carried out. An interscale connectivity graph is then established. In accordance with some rules that permit false detections to be removed, the objects and their sub-objects are identified. They define respectively trees and sub-trees in the graph. In this way, the identification of the WTS pixels of the tree related to a given object leads to the reconstruction of its image by the conjugate gradient method. The model has been tested successfully on simulated images of stars and galaxies which allow us to show the capabilities of the detection and restoration procedures of the model. Finally, tests on real images show that one can analyse complex structures better than with classical astronomical vision models.

Measuring accurate graph snapshots of peer-to-peer (P2P) overlay networks is essential to understand these systems. Furthermore, the captured graph snapshots can be used, among other important purposes, as traces for simulation studies, to validate existing simulation models, to design and implement targeted attacks, or to detect anomalies. Motivated by the importance of the purposes above as well as the popularity of several Kademlia-like networks, we present a new crawler aiming to capture snapshots of the connectivity graph of the entire KAD network. The crawler's design is generic and adaptable for Kademlia-like and other structured P2P networks. The results show that the crawler is fast and captures high accurate graph snapshots. Furthermore, its design enables it to outperform prior KAD crawlers significantly in terms of the time and the number of crawling messages that are required to download nodes' routing tables. The crawls that we conducted at different times between April 2012 and February 2013 show that KAD is still widely-used in terms of total observed users. However, when compared to the results of prior studies, we report a significant drop in the number of its simultaneous online users.

We use an evolutionary algorithm (EA) to search the space of leaky integrate-and-fire (LIF) neuron networks, in order to identify network connectivities producing significant rhythmic activity at sub-spiking frequencies (i.e., 'bursting-like behavior'). We find that the connectivities of the most-fit LIF networks exhibit a relatively broad in-degree distribution and a relatively narrow out-degree distribution. We examine the frequencies of connection motifs in the most-fit LIF networks as compared to random networks. In a network of more realistically modeled neurons, the most-fit network connectivities are observed to produce a broader frequency response as compared to that resulting from random network connectivities. Figure ​Figure11 shows the cumulative in- and out-degree distributions of a representative most-fit network, whose connectivity graph is illustrated in the inset. The orange line represents the cumulative in-degree distribution, and the red line the cumulative out-degree distribution. The blue bars represent the expected cumulative distribution (and error bars) for random network connectivities. Figure 1 shows the cumulative in- and out-degree distributions of a representative most-fit network, whose connectivity graph is illustrated in the inset. The orange line represents the cumulative in-degree distribution, and the red line the cumulative out-degree ...

Indoor Location-Based Services (LBS) inevitably require the map of the target indoor environment. Manually designed map is generally used for the purpose in the legacy indoor LBS application. However, this approach is not easy to acquire the dynamic knowledge from the environment such as preferred moving path, traffic of walkers and temporal blocked area. In this paper, we proposed a new method that automatically collects the room connectivity graph with the distance and traffic factor as weight of edge. In order to achieve the dynamic information, we use the actual travel path of the mobile node worn by a resident of the indoor environment. As the experimental evaluation, our proposed method could easily achieve the dynamic indoor map information only using the self-organized interactions between stationary nodes and mobile nodes, such as watch, worn by residents.

We have implemented a multiscale vision model based on the wavelet transform to analyse field astronomical images. The discrete transform is performed by the a trous algorithm. The vision model is based on the notion of the significant structures. We identify the pixels of the wavelet transform space (WTS) associated with the objects. At each scale a region labelling is carried out. An interscale connectivity graph is then established. In accordance with some rules that permit false detections to be removed, the objects and their sub-objects are identified. They define respectively trees and sub-trees in the graph. So, the identification of the WTS pixels of the tree related to a given object leads to the reconstruction of its image by the conjugate gradient method. The model has been tested successfully on astronomical images which shows that complex structures are better analysed than using usual astronomical vision models.

Electric stimulation has been widely used to induce changes in neuronal cultures coupled to microelectrode arrays (MEAs). In this paper, we used different electrical stimulation protocols on dissociated cultures of hippocampal cells for studying the electrical properties of the process. We show that persistent and synchronous stimulation of adjacent electrodes may be used for creating adjacent physical or logical connections in the connectivity graph following Hebb’s Law modifying the neural responses principal parameters.

In this paper, we propose an indoor data model named Indoor Navigation Space Model (INSM). It is designed to support automatic derivation of the connectivity graph of a building. The INSM model provides an extended categorization of indoor spaces based on building semantics. It can be used to specify the nodes and edges of the connectivity graph. A UML class diagram of the INSM and a general approach of external data conversion are provided as well. An initial test of indoor path-finding is conducted and it demonstrates the feasibility of the INSM model to support indoor routing.

The different techniques for generation of sequences of assembly for multi robotic assembly cell problems are proposed in this research work. The objective of the research work is to develop the sequence for the robotic assembly by using Connectivity Graph and Liaison Method using LINGO. A computer-based, generic, scalable and integrated optimization method for generation of assembly sequence is developed and tested for the benefit of the professionals. Finally, the results proposed using the two methods are compared and the global optimum solution is proposed with a view to achieving concurrency for the robotic assembly activities and making the system more economical.

This note studies a network of agents having continuous-time dynamics with quantized interactions and time-varying directed topology. Due to the discontinuity of the dynamics, solutions of the resulting ODE systems are intended in the sense of Krasovskii. A limit connectivity graph is defined, which encodes persistent interactions between nodes: if such graph has a globally reachable node, Krasovskii solutions reach consensus (up to the quantizer precision) after a finite time. Under the additional assumption of a time-invariant topology, the convergence time is upper bounded by a quantity which depends on the network size and the quantizer precision. It is observed that the convergence time can be very large for solutions which stay on a discontinuity surface.

Crimes rarely occur if potential offenders are aware that there are eyes on the street that can witness, report or intervene in their activities. Lighting has a major role in guarding against criminal assault on public streets at night. The lighting needs to illuminate more than just the actual lighting functions; it also shapes the face of the street and defines the residential environment as a “homely” atmosphere. The residential area in a traditional city is in particular considered in this study. Space syntax models spatial configurations of urban street configurations by using a connectivity graph representation. Such a configuration of connectivity identifies pedestrian access patterns and can be analyzed and selected in a study area. The illumination distribution is calculated and a lighting design framework is suggested.

We study the utility of dynamic frequency agility in real-world wireless sensor networks. Many view such agility as essential to obtaining adequate reliability in industrial environments. We quantify the actual utility by identifying the two facets of connectivity graphs that yield potential benefits called Multichannel Links (MCLs) and Multichannel Triangles (MCTs), study how frequently these occur empirically and determine whether multihop provides a comparable solution without the complexity of switching channels. We examine connectivity graphs of live networks over each 802.15.4 channel and find that MCLs and MCTs are extremely rare in practice. Almost no MCLs are found in any connectivity graph while MCTs occur between 0-200 parts per million (ppm). Furthermore, we show that MCLs are rarely important for routing while each MCT has a singlechannel routing solution. We also find that there are channels that are always good for connectivity and offer comparable routing costs, with respect to transmission count, in comparison to multichannel communication. Thus, the justification for channel agility in industrial environments applies in the absence but not in the presence of multihop routing.

We present a novel design procedure for translucent optical networks. Regenerating nodes are selected via an Impairment-Aware ILP formulation exploiting the so-called “connectivity graph”.

To construct a similarity graph we transform a given set x1,...,x n of data points with pairwise similarities s ij or distances d ij into a graph. There are several popular methods to construct similarity graphs [154]. The goal of constructing similarity graphs is to model the local neighborhood relationships between data points. In this section we review two popular methods to construct a similarity graph, and then we introduce a new algorithm that solves some problems that can not be solved by the others.

To evaluate peer-to-peer systems through discrete-event simulation, one needs to be able to generate sufficiently large networks of nodes that exhibit the desired properties, such as the scale-free nature of the connectivity graph. In applications such as the web of trust or analysis of hyperlink structures, the direction of the arcs between two nodes is relevant and one therefore generates directed graphs. In this paper we introduce a model to generate directed scale free graphs without multiple arcs between the same pair of nodes and loops. This model is based on existing models that allows multiple arcs and loops, but considerably more challenging to implement in an efficient manner. We therefore design and implement a set of algorithms and compare them with respect to CPU and memory use, in terms of both theoretical complexity analysis and experimental results. We will show through experiments that with the fastest algorithms networks with a million or more nodes can be generated in mere seconds.

Navigation in complex and large-scale 3D virtual environments has been shown to be a difficult task, imposing a high cognitive load on the user. In this paper, we present a comprehensive method for assisting users in exploring and understanding such 3D worlds. The method consists of two distinct phases: an off-line computation step deriving a grand tour using the world geometry and any semantic target information as input, and an on-line interactive navigation step providing guided exploration and improved spatial perception for the user. The former phase is based on a voxelized version of the geometrical dataset that is used to compute a connectivity graph for use in a TSP-like formulation of the problem. The latter phase takes the output tour from the off-line step as input for guiding 3D navigation through the environment.

We consider the problem of throughput-optimal scheduling in wireless networks subject to interference constraints. We model the interference using a family of K -hop interference models. We define a K-hop interference model as one for which no two links within K hops can successfully transmit at the same time (Note that IEEE 802.11 DCF corresponds to a 2-hop interference model.) .For a given K, a throughput-optimal scheduler needs to solve a maximum weighted matching problem subject to the K-hop interference constraints. For K=1, the resulting problem is the classical Maximum Weighted Matching problem, that can be solved in polynomial time. However, we show that for K>1,the resulting problems are NP-Hard and cannot be approximated within a factor that grows polynomially with the number of nodes. Interestingly, we show that for specific kinds of graphs, that can be used to model the underlying connectivity graph of a wide range of wireless networks, the resulting problems admit polynomial time approximation schemes. We also show that a simple greedy matching algorithm provides a constant factor approximation to the scheduling problem for all K in this case. We then show that under a setting with single-hop traffic and no rate control, the maximal scheduling policy considered in recent related works can achieve a constant fraction of the capacity region for networks whose connectivity graph can be represented using one of the above classes of graphs. These results are encouraging as they suggest that one can develop distributed algorithms to achieve near optimal throughput in case of a wide range of wireless networks.

Manifold parameterization considers the problem of parameterizing a given triangular mesh onto another mesh surface, which could be particularly plane or sphere surfaces. In this paper we propose a unified framework for manifold parameterization between arbitrary meshes with identical genus. Our approach does this task by directly mapping the connectivity of the source mesh onto the target mesh surface without any intermediate domain and partition of the meshes. The connectivity graph of source mesh is used to approximate the geometry of target mesh using least squares meshes. A subset of user specified vertices are constrained to have the geometry information of the target mesh. The geometry of the mesh vertices is reconstructed while approximating the known geometry of the subset by positioning each vertex approximately at the center of its immediate neighbors. This leads to a sparse linear system which can be effectively solved. Our approach is simple and fast with less user interactions. Many experimental results and applications are presented to show the applicability and flexibility of the approach.

A new method to obtain a rough description of a 3-D object from its outline shape is presented in this paper. Firstly the outline shape is split up into parts that are related in a connectivity graph. The boundary features of every part (lines, curves) and the information about its junction with other parts are extracted in order to provide a semantic content to the outline shape graph. A specific language to describe this graph has been developed. Finally, some results obtained applying our approach over real images are presented and discussed.

The interdependencies of parameters applied in the models of EUSES are visualised in a directed connectivity graph. The parameters (inputs, defaults, state variables, outputs) are represented by boxes (nodes) and their relations by lines (edges). The visualisation, on the one hand, clarifies the complexity of the models in EUSES and, on the other hand, creates an overview and transparency. The parameters' relations to each other can be recognised faster, and the models can be better understood. The complexity was quantified by the number (variety), kind (substance parameter, physico-chemical parameter, concentration, other parameters), and depth (dimension) of the parameter and the number of relations (connectivity). The variety of EUSES (without the models Simple Treat and Simple Box whose interior structure is not documented and without the effect and risk characterisation) amounts to 466, the connectivity to 961, and the maximal dimension is 21.