Variable penalty factors: a new GEP automatic clustering algorithm

2017 ◽  
Vol 15 (1/2) ◽  
pp. 3
Author(s):  
Haohua Huang ◽  
Yan Chen ◽  
Kangshun Li
Keyword(s):  
Clustering Algorithm ◽  
Automatic Clustering

Segmentation of SAR Image using Fuzzy C-Means and Filters

2020 ◽  
Vol 8 (1) ◽  
pp. 84-90
Author(s):  
R. Lalchhanhima ◽  
◽  
Debdatta Kandar ◽  
R. Chawngsangpuii ◽  
Vanlalmuansangi Khenglawt ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Spatial Filters ◽  
Fuzzy C Means ◽  
Automatic Clustering ◽  
Intensity Information

Fuzzy C-Means is an unsupervised clustering algorithm for the automatic clustering of data. Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore the segmentation process can not directly rely on the intensity information alone but must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use the fuzzy nature of classification for the purpose of unsupervised region segmentation in which FCM is employed. Different features are obtained by filtering of the image by using different spatial filters and are selected for segmentation criteria. The segmentation performance is determined by the accuracy compared with a different state of the art techniques proposed recently.

A population-based automatic clustering algorithm for image segmentation

2021 ◽  
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Gerald Schaefer ◽  
Mahshid Helali Moghadam ◽  
Mehrdad Saadatmand ◽  
Mahdi Pedram
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Population Based ◽  
Automatic Clustering

scholarly journals Wideband Channel Modeling for mm-Wave inside Trains for 5G-Related Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Juan Moreno García-Loygorri ◽  
César Briso-Rodríguez ◽  
Israel Arnedo ◽  
César Calvo ◽  
Miguel A. G. Laso ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Path Loss ◽  
Channel Modeling ◽  
Wireless Channel ◽  
Full Description ◽  
Model Parameters ◽  
Time Of Arrival ◽  
Traffic Demand ◽  
Automatic Clustering ◽  
Wideband Channel

Passenger trains and especially metro trains have been identified as one of the key scenarios for 5G deployments. The wireless channel inside a train car is reported in the frequency range between 26.5 GHz and 40 GHz. These bands have received a lot of interest for high-density scenarios with a high-traffic demand, two of the most relevant aspects of a 5G network. In this paper we provide a full description of the wideband channel estimating Power-Delay Profiles (PDP), Saleh-Valenzuela model parameters, time-of-arrival (TOA) ranging, and path-loss results. Moreover, the performance of an automatic clustering algorithm is evaluated. The results show a remarkable degree of coherence and general conclusions are obtained.

Instruction Clustering Analysis for Unknown Network Protocol's Abnormal Behavior

2015 ◽  
Vol 15 (03n04) ◽  
pp. 1540002
Author(s):  
YANJING HU ◽  
QINGQI PEI ◽  
LIAOJUN PANG
Keyword(s):  
Clustering Analysis ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Abnormal Behavior ◽  
Automatic Clustering ◽  
Reverse Analysis ◽  
Message Format ◽  
Instruction Sequences ◽  
Analysis Platform ◽  
Abnormal Behaviors

Protocol's abnormal behavior analysis is an important task in protocol reverse analysis. Traditional protocol reverse analysis focus on the protocol message format, but protocol behavior especially the abnormal behavior is rare studied. In this paper, protocol behavior is represented by the labeled behavior instruction sequences. Similar behavior instruction sequences mean the similar protocol behavior. Using our developed virtual analysis platform HiddenDisc, we can capture a variety of known or unknown protocols' behavior instruction sequences. All kinds of executed or unexecuted instruction sequences can automatic clustering by our designed instruction clustering algorithm. Thereby we can distinguish and mine the unknown protocols' potential abnormal behavior. The mined potential abnormal behavior instruction sequences are executed, monitored and analyzed on HiddenDisc to determine whether it is an abnormal behavior and what is the behavior's nature. Using the instruction clustering algorithm, we have analyzed 1297 protocol samples, mined 193 potential abnormal instruction sequences, and determined 187 malicious abnormal behaviors by regression testing. Experimental results show that our proposed instruction clustering algorithm has high efficiency and accuracy, can mine unknown protocols' abnormal behaviors effectively, and enhance the initiative defense capability of network security.

scholarly journals A New Fuzzy Rule Based Contrast Enhancement Method using The Two-Steps Automatic Clustering Algorithm

2018 ◽  
Vol 2 (4) ◽  
pp. 239
Author(s):  
Ha Che-Ngoc ◽  
Anh-Thy Pham-Chau ◽  
Dibya Jyoti Bora
Keyword(s):  
Contrast Enhancement ◽  
Clustering Algorithm ◽  
Fuzzy Rule ◽  
Pixel Intensity ◽  
Rule Based ◽  
Creative Commons ◽  
Automatic Clustering ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Human Eyes

The contrast is a major factor influencing the image quality; therefore, image contrast enhancement technique is more and more widely applied in the field of image processing. In this paper, a new fuzzy rule-based contrast enhancement method using the two-steps automatic clustering algorithm is proposed. Specifically, based on the Automatic clustering algorithm, a state-of-art method in cluster analysis and data mining, this paper proposes a two-steps Automatic clustering method to determine the number of fuzzy sets and locate the critical point in membership functions so that they are suitable for the distribution of pixel intensity values. The experiments on the "Lena" image and other natural images demonstrate that the new method can effectively enhance the contrast of the images and meet the demands of human eyes perception at the same time.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

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