MCRM System: CIM-Based Multiple Clusters Manager

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

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3GPP Channel Model Emulation with Analysis of MIMO-LTE Performances in Reverberation Chamber

International Journal of Antennas and Propagation ◽

10.1155/2012/239420 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8 ◽

Cited By ~ 15

Author(s):

Nabil Arsalane ◽

Moctar Mouhamadou ◽

Cyril Decroze ◽

David Carsenat ◽

Miguel Angel Garcia-Fernandez ◽

...

Keyword(s):

Channel Model ◽

Reference Case ◽

Reverberation Chamber ◽

Single Input Single Output ◽

Single Output ◽

Received Power ◽

Communication Devices ◽

Set Up ◽

Multiple Clusters ◽

Active Link

Emulation methodology of multiple clusters channels for evaluating wireless communication devices over-the-air (OTA) performance is investigated. This methodology has been used along with the implementation of the SIMO LTE standard. It consists of evaluating effective diversity gain (EDG) level of SIMO LTE-OFDM system for different channel models according to the received power by establishing an active link between the transmitter and the receiver. The measurement process is set up in a Reverberation Chamber (RC). The obtained results are compared to the reference case of single input-single output (SISO) in order to evaluate the real improvement attained by the implemented system.

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The ecology of the galactic centre: Nuclear stellar clusters and supermassive black holes

Proceedings of the International Astronomical Union ◽

10.1017/s1743921319006689 ◽

2019 ◽

Vol 14 (S351) ◽

pp. 80-83 ◽

Cited By ~ 1

Author(s):

Melvyn B. Davies ◽

Abbas Askar ◽

Ross P. Church

Keyword(s):

Black Holes ◽

Black Hole ◽

Large Fraction ◽

Galactic Nuclei ◽

Supermassive Black Holes ◽

Galactic Centre ◽

Stellar Clusters ◽

Stellar Cluster ◽

Supermassive Black Hole ◽

Multiple Clusters

AbstractSupermassive black holes are found in most galactic nuclei. A large fraction of these nuclei also contain a nuclear stellar cluster surrounding the black hole. Here we consider the idea that the nuclear stellar cluster formed first and that the supermassive black hole grew later. In particular we consider the merger of three stellar clusters to form a nuclear stellar cluster, where some of these clusters contain a single intermediate-mass black hole (IMBH). In the cases where multiple clusters contain IMBHs, we discuss whether the black holes are likely to merge and whether such mergers are likely to result in the ejection of the merged black hole from the nuclear stellar cluster. In some cases, no supermassive black hole will form as any merger product is not retained. This is a natural pathway to explain those galactic nuclei that contain a nuclear stellar cluster but apparently lack a supermassive black hole; M33 being a nearby example. Alternatively, if an IMBH merger product is retained within the nuclear stellar cluster, it may subsequently grow, e.g. via the tidal disruption of stars, to form a supermassive black hole.

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Fast Search Method Based on Vector Quantization for Raman Spectroscopy Identification

Mathematics ◽

10.3390/math8111970 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1970

Author(s):

Jun-Kyu Park ◽

Suwoong Lee ◽

Aaron Park ◽

Sung-June Baek

Keyword(s):

Search Algorithm ◽

Principal Component ◽

Search Method ◽

Reference Spectrum ◽

Search Range ◽

Fast Search ◽

Input Spectrum ◽

Computationally Intensive ◽

Multiple Clusters ◽

Full Search

In spectroscopy, matching a measured spectrum to a reference spectrum in a large database is often computationally intensive. To solve this problem, we propose a novel fast search algorithm that finds the most similar spectrum in the database. The proposed method is based on principal component transformation and provides results equivalent to the traditional full search method. To reduce the search range, hierarchical clustering is employed, which divides the spectral data into multiple clusters according to the similarity of the spectrum, allowing the search to start at the cluster closest to the input spectrum. Furthermore, a pilot search was applied in advance to further accelerate the search. Experimental results show that the proposed method requires only a small fraction of the computational complexity required by the full search, and it outperforms the previous methods.

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One-IPC high-level simulation of microthreaded many-core architectures

The International Journal of High Performance Computing Applications ◽

10.1177/1094342015584495 ◽

2016 ◽

Vol 31 (2) ◽

pp. 152-162 ◽

Cited By ~ 3

Author(s):

Irfan Uddin

Keyword(s):

Design Space Exploration ◽

Instruction Set ◽

Efficient Design ◽

Simulation Framework ◽

Fine Grained ◽

Detailed Simulation ◽

High Level ◽

Many Core ◽

The Cost ◽

Multiple Clusters

The microthreaded many-core architecture is comprised of multiple clusters of fine-grained multi-threaded cores. The management of concurrency is supported in the instruction set architecture of the cores and the computational work in application is asynchronously delegated to different clusters of cores, where the cluster is allocated dynamically. Computer architects are always interested in analyzing the complex interaction amongst the dynamically allocated resources. Generally a detailed simulation with a cycle-accurate simulation of the execution time is used. However, the cycle-accurate simulator for the microthreaded architecture executes at the rate of 100,000 instructions per second, divided over the number of simulated cores. This means that the evaluation of a complex application executing on a contemporary multi-core machine can be very slow. To perform efficient design space exploration we present a co-simulation environment, where the detailed execution of instructions in the pipeline of microthreaded cores and the interactions amongst the hardware components are abstracted. We present the evaluation of the high-level simulation framework against the cycle-accurate simulation framework. The results show that the high-level simulator is faster and less complicated than the cycle-accurate simulator but with the cost of losing accuracy.

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The fuzzy structure of populations

Canadian Journal of Zoology ◽

10.1139/z02-184 ◽

2002 ◽

Vol 80 (12) ◽

pp. 2235-2241 ◽

Cited By ~ 24

Author(s):

James A Schaefer ◽

Chris C Wilson

Keyword(s):

Rangifer Tarandus ◽

Human Perception ◽

Fuzzy Classification ◽

Resolving Power ◽

Biological Organization ◽

Philosophical Foundations ◽

Living Things ◽

Biological World ◽

Multiple Clusters ◽

Management And Conservation

The human perception of biological organization has profound implications for the study, management, and conservation of living things. Traditional methods of classification, which imply all-or-nothing group membership, are inconsistent with the modern synthesis, which stresses variability and unique individuals. We propose that fuzzy classification, which allows fractional membership in multiple clusters, can more realistically denote many forms of biological organization, such as populations. We used fuzzy clustering to depict the ambiguous structure of a migratory caribou (Rangifer tarandus) herd, based on affinities in space use, and walleye (Stizostedion vitreum) stocks, based on genetic dissimilarities among multilocus genotypes. In both cases, fuzzy memberships conveyed the degree of uncertainty of belonging while resolving cluster memberships for unambiguous and problematic individuals. Vagueness implies that borderline group identity cannot be remedied with more resolving power. Fuzzy classification is more in tune with the empirical and philosophical foundations of our discipline and can reconcile our need to classify with an inherently vague biological world.

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A two-stage clustering-based cold-start method for active learning

Intelligent Data Analysis ◽

10.3233/ida-205393 ◽

2021 ◽

Vol 25 (5) ◽

pp. 1169-1185

Author(s):

Deniu He ◽

Hong Yu ◽

Guoyin Wang ◽

Jie Li

Keyword(s):

Active Learning ◽

Class Imbalance ◽

Imbalanced Data ◽

Cold Start ◽

Classification Performance ◽

The Novel ◽

Two Stage ◽

Minority Class ◽

Novel Method ◽

Multiple Clusters

The problem of initialization of active learning is considered in this paper. Especially, this paper studies the problem in an imbalanced data scenario, which is called as class-imbalance active learning cold-start. The novel method is two-stage clustering-based active learning cold-start (ALCS). In the first stage, to separate the instances of minority class from that of majority class, a multi-center clustering is constructed based on a new inter-cluster tightness measure, thus the data is grouped into multiple clusters. Then, in the second stage, the initial training instances are selected from each cluster based on an adaptive candidate representative instances determination mechanism and a clusters-cyclic instance query mechanism. The comprehensive experiments demonstrate the effectiveness of the proposed method from the aspects of class coverage, classification performance, and impact on active learning.

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