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This paper examines a schema for graph-theoretic clustering using node-based resilience measures. Node-based resilience measures optimize an objective based on a critical set of nodes whose removal causes some severity of disconnection in the network. Beyond presenting a general framework for the usage of node based resilience measures for variations of clustering problems, we emphasize the unique...
Superpixel algorithm aims to semantically group neighboring pixels into a coherent region. It could significantly boost the performance of the subsequent vision processing task such as image segmentation. Recently, the work simple linear iterative clustering (SLIC) [1] has drawn huge attention for its state-of-the-art segmentation performance and high computational efficiency. However, the performance...
Clustering is applied to many applications and the decision with regards to which algorithm to use is dependent on the nature of the task to be carried out. Before choosing which clustering algorithm to use one needs to be aware of the nature of the task to be done and then determine the algorithm accordingly, based on the capabilities and performance metrics of that algorithm. This paper makes an...
To segment multi-spectral remote sensor images, feature extraction and object classification is an essential step that performs region-based segmentation instead of a pixel-based segmentation. Spectral based segmentation methods like K-Means, Mean-shift segmentation fail to extract optimal regions from multi-spectral images. In high-resolution multi-spectral images, segmentation main aim is to divide...
In this paper, we present a scene recognition framework, which could process the images and recognize the scene in the images. We demonstrate and evaluate the performance of our system on a dataset of Oxford typical landmarks. In this paper, we put forward a novel method of local k-meriod for building a vocabulary and introduce a novel quantization method of soft-assignment based on the Gaussian mixture...
In this paper, we consider a special multi-source data clustering problem for which the data-points from the same source cannot be grouped into the same cluster, namely cannot link (CL) constraint, and the sizes of the generated clusters are subject to maximum thresholds. No prior information is given about the level of clutter (namely noisy data) or the number of clusters. Particularly, the clusters...
Synchronization in message passing systems is achieved by communication among processes. System and architectural noise and different workloads cause processes to be imbalanced and to reach synchronization points at different time. Thus, both communication and imbalance impact the synchronization performance. In this paper, we study the algorithmic properties that allow the communication in synchronization...
Simulating the time-variance of vehicular channels correctly remains a challenging topic. We are interested in parsimonious mathematical channel models, in which only significant groups of multipath components (MPCs) are included. The MPCs are grouped in the delay-Doppler domain, which enables the development of cluster-based channel models. However, the characterization of time-variant vehicular...
Cluster based channel models can be used to reduce the computational complexity. For vehicular communications, the environment changes rapidly due to the high velocities of the transmitter and receiver, resulting in fast changing cluster parameters. In this paper, we present an automatic cluster identification and tracking algorithm in order to consistently characterize the evolution of cluster parameters,...
Interaction analysis is defined as the generation of semantic descriptions from machine perception. This can be achieved through a combination of fuzzy metric temporal logic (FMTL) and situation graph trees (SGTs). We extended the FMTL/SGT framework with modules for clustering and parameter learning and we showed their advantages. The contributions of this paper are 1) the combination of FMTL/SGT...
Hydro and Agro Informatics Institute (HAII) has installed more than 800 telemetry stations across Thailand to collect water level data for operation tasks and researches, e.g., flooding prevention system. To have an accurate result, it is crucial to control the quality of data by detecting and filtering out anomalies. In our previous work, a data quality management system to capture various types...
In this paper we demonstrate a new density based clustering technique, CODSAS, for online clustering of streaming data into arbitrary shaped clusters. CODAS is a two stage process using a simple local density to initiate micro-clusters which are then combined into clusters. Memory efficiency is gained by not storing or re-using any data. Computational efficiency is gained by using hyper-spherical...
We consider the problem of clustering noisy finite-length observations of stationary ergodic random processes according to their nonparametric generative models without prior knowledge of the model statistics and the number of generative models. Two algorithms, both using the L1-distance between estimated power spectral densities (PSDs) as a measure of dissimilarity, are analyzed. The first algorithm,...
Towards safe autonomous vehicle navigation the problem of lane detection and classification is highly important in the development of advanced driver assistance system (ADAS). This paper proposes a new method to detect the road lane marking for safe autonomous navigation purpose. It focuses on unconventional methods of identifying lane markings on a road surface through Laser Measurement System (LMS)...
Synchrophasors are the state-of-the-art measuring sensors that sense voltage, current, or frequency with high data rate. This paper presents an approach to analyze the streaming smart-grid data generated by synchrophasors. A novel unit-circle representation is used to visualize the real-time phasor data. A Density based clustering (DBSCAN) method is proposed to cluster the phasor data to detect bad-data...
We describe the Fast Greedy Sparse Subspace Clustering (FGSSC) algorithm providing an efficient method for clustering data belonging to a few low-dimensional linear or affine subspaces. FGSSC is a modification of the SSC algorithm. The main difference of our algorithm from predecessors is its ability to work with noisy data having a high rate of erasures (missed entries at the known locations) and...
Polycystic Ovary Syndrome (PCOS) is the most common endocrine disorders affected to female in their reproductive cycle. PCO (Polycystic Ovaries) describes ovaries that contain many small cysts/follicles. This paper proposes an image clustering approach for follicles segmentation using Particle Swarm Optimization (PSO) with a new modified non-parametric fitness function. The new modified fitness function...
Building accurate bathymetries of the seabed has been a focus of study in the last decade. For this purpose seabed point cloud registration has been a focus for some researchers. Some of this registration methods are based on gathering the points of the cloud that contain more information for the registration (i.e. the ones that flat or smooth, normally being the seabed) and using them as part of...
A spatially constrained kernel fuzzy C-means (SCKFCM) algorithm is represented for polarimetric SAR (PolSAR) remote sensing image segmentation in this paper. Compared with classic fuzzy C-means (FCM) algorithm, kernel method could perform the nonlinear mapping from the original space to kernel space. Thus, SCKFCM is not impacted by the remote sensing image data distribution. Furthermore, in order...
Twitter enables users to write and publish messages with a maximum of 140 characters. This is sometimes termed micro-blogging because individuals often use Twitter to communicate their thoughts, commentary or feelings about any given subject. Twitter's significant popularity and mass usage has resulted in any subject queried from the Twitter API that may return a vast number of tweets. These tweets...
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