scholarly journals Clustering Algorithms and Validation Indices for a Wide mmWave Spectrum

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 287 ◽  
Author(s):  
Bogdan Antonescu ◽  
Miead Tehrani Moayyed ◽  
Stefano Basagni
Keyword(s):  
Communication Systems ◽  
Clustering Algorithm ◽  
Radio Channel ◽  
Clustering Algorithms ◽  
Wireless Communication Systems ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Wide Range ◽  
Radio Signals ◽  
Urban Scenario

Radio channel propagation models for the millimeter wave (mmWave) spectrum are extremely important for planning future 5G wireless communication systems. Transmitted radio signals are received as clusters of multipath rays. Identifying these clusters provides better spatial and temporal characteristics of the mmWave channel. This paper deals with the clustering process and its validation across a wide range of frequencies in the mmWave spectrum below 100 GHz. By way of simulations, we show that in outdoor communication scenarios clustering of received rays is influenced by the frequency of the transmitted signal. This demonstrates the sparse characteristic of the mmWave spectrum (i.e., we obtain a lower number of rays at the receiver for the same urban scenario). We use the well-known k-means clustering algorithm to group arriving rays at the receiver. The accuracy of this partitioning is studied with both cluster validity indices (CVIs) and score fusion techniques. Finally, we analyze how the clustering solution changes with narrower-beam antennas, and we provide a comparison of the cluster characteristics for different types of antennas.

scholarly journals Design of an Unsupervised Machine Learning-Based Movie Recommender System

2020 ◽  
Author(s):  
Debby Cintia Ganesha Putri ◽  
Jenq-Shiou Leu ◽  
Pavel Seda
Keyword(s):  
Recommender System ◽  
Clustering Algorithm ◽  
System Development ◽  
Clustering Algorithms ◽  
Mean Shift ◽  
Computational Time ◽  
Agglomerative Clustering ◽  
Method Performance ◽  
Cluster Validity Indices ◽  
Validity Indices

This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We propose methods optimizing K so that each cluster may not significantly increase variance. We are limited to using groupings based on Genre and, Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and Betweenness Centrality. We also used Average Similarity, Computational Time, Association Rule with Apriori algorithm, and Clustering Performance Evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and Davies-Bouldin Index.

scholarly journals Design of an Unsupervised Machine Learning-Based Movie Recommender System

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 185 ◽  
Author(s):  
Debby Cintia Ganesha Putri ◽  
Jenq-Shiou Leu ◽  
Pavel Seda
Keyword(s):  
Recommender System ◽  
Clustering Algorithm ◽  
System Development ◽  
Clustering Algorithms ◽  
Mean Shift ◽  
Computational Time ◽  
Agglomerative Clustering ◽  
Method Performance ◽  
Cluster Validity Indices ◽  
Validity Indices

This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We propose methods optimizing K so that each cluster may not significantly increase variance. We are limited to using groupings based on Genre and Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and Betweenness Centrality. We also used average similarity, computational time, association rule with Apriori algorithm, and clustering performance evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and Davies–Bouldin Index.

scholarly journals Role of Cluster Validity Indices in Delineation of Precipitation Regions

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1372
Author(s):  
Nikhil Bhatia ◽  
Jency M. Sojan ◽  
Slobodon Simonovic ◽  
Roshan Srivastav
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Ratio Test ◽  
Cluster Validity ◽  
Number Of Clusters ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Point Data ◽  
Optimal Number Of Clusters

The delineation of precipitation regions is to identify homogeneous zones in which the characteristics of the process are statistically similar. The regionalization process has three main components: (i) delineation of regions using clustering algorithms, (ii) determining the optimal number of regions using cluster validity indices (CVIs), and (iii) validation of regions for homogeneity using L-moments ratio test. The identification of the optimal number of clusters will significantly affect the homogeneity of the regions. The objective of this study is to investigate the performance of the various CVIs in identifying the optimal number of clusters, which maximizes the homogeneity of the precipitation regions. The k-means clustering algorithm is adopted to delineate the regions using location-based attributes for two large areas from Canada, namely, the Prairies and the Great Lakes-St Lawrence lowlands (GL-SL) region. The seasonal precipitation data for 55 years (1951–2005) is derived using high-resolution ANUSPLIN gridded point data for Canada. The results indicate that the optimal number of clusters and the regional homogeneity depends on the CVI adopted. Among 42 cluster indices considered, 15 of them outperform in identifying the homogeneous precipitation regions. The Dunn, D e t _ r a t i o and Trace( W − 1 B ) indices found to be the best for all seasons in both the regions.

scholarly journals Compact Rectifier Circuit Design for Harvesting GSM/900 Ambient Energy

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1614
Author(s):  
Surajo Muhammad ◽  
Jun Jiat Tiang ◽  
Sew Kin Wong ◽  
Amjad Iqbal ◽  
Mohammad Alibakhshikenari ◽  
...  
Keyword(s):  
Circuit Design ◽  
Communication Systems ◽  
Path Loss ◽  
Input Power ◽  
Wireless Communication Systems ◽  
Rf Power ◽  
Total Size ◽  
High Input Power ◽  
Wide Range ◽  
Rf Energy

In this paper, a compact rectifier, capable of harvesting ambient radio frequency (RF) power is proposed. The total size of the rectifier is 45.4 mm × 7.8 mm × 1.6 mm, designed on FR-4 substrate using a single-stage voltage multiplier at 900 MHz. GSM/900 is among the favorable RF Energy Harvesting (RFEH) energy sources that span over a wide range with minimal path loss and high input power. The proposed RFEH rectifier achieves measured and simulated RF-to-dc (RF to direct current) power conversion efficiency (PCE) of 43.6% and 44.3% for 0 dBm input power, respectively. Additionally, the rectifier attained 3.1 V DC output voltage across 2 kΩ load terminal for 14 dBm and is capable of sensing low input power at −20 dBm. The work presents a compact rectifier to harvest RF energy at 900 MHz, making it a good candidate for low powered wireless communication systems as compares to the other state of the art rectifier.

scholarly journals A Real Case of Implementation of the Future 5G City

2018 ◽  
Vol 11 (1) ◽  
pp. 4 ◽  
Author(s):  
Dania Marabissi ◽  
Lorenzo Mucchi ◽  
Romano Fantacci ◽  
Maria Spada ◽  
Fabio Massimiani ◽  
...  
Keyword(s):  
Communication Systems ◽  
Social Impact ◽  
Field Trials ◽  
Use Cases ◽  
Wireless Communication Systems ◽  
Performance Requirements ◽  
Fifth Generation ◽  
Wide Range ◽  
5G Network ◽  
The Future

The fifth generation (5G) of wireless communication systems is considered the key technology to enable a wide range of application scenarios and the effective spreading of the smart city concept. Vertical business use cases, specifically designed for the future 5G city, will have a strong economical and social impact. For this reason, ongoing 5G field trials have to test newly deployed technologies as well as the capability of 5G to create a new digital economy. This paper describes the 5G field trial environment that was launched in Italy at the end of 2017. The aim is to evaluate the capability of the 5G network of supporting innovative services with reference to suitably designed key performance indicators and to evaluate the opportunities offered by these services. Indeed, vertical business use cases, specifically designed for the future 5G city, with a strong economic and social impact, are under implementation and will be evaluated. In particular, the paper provides a detailed description of the deployment of an actual complete integrated 5G network. It shows how 5G is effective enabling technology for a wide range of vertical business and use cases. Indeed, its flexibility allows to satisfy completely different performance requirements of real services. Some preliminary results, obtained during the first phase, are presented for a smart mobility scenario.

scholarly journals Assessment of some combinations of hard and fuzzy clustering techniques for regionalisation of catchments in Sefidroud basin

2016 ◽  
Vol 18 (6) ◽  
pp. 1033-1054 ◽  
Author(s):  
Ali Ahani ◽  
S. Saeid Mousavi Nadoushani
Keyword(s):  
Objective Function ◽  
Clustering Algorithms ◽  
Hybrid Approach ◽  
Hybrid Algorithms ◽  
Flood Frequency Analysis ◽  
Cluster Validity ◽  
Fcm Algorithm ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Regional Flood Frequency Analysis

Cluster analysis methods are a type of well-known technique for regionalisation of catchments to perform regional flood frequency analysis. In this study, a fuzzy extension of hybrid clustering algorithms is evaluated. Self-organizing feature maps and four hierarchical clustering algorithms were used to provide the initial cluster centres for fuzzy c-means (FCM) algorithm. The hybrid approach was used for regionalisation of catchments in Sefidroud basin based on feature vectors including five catchment attributes: longitude and latitude, drainage area, runoff coefficient and mean annual precipitation. The results showed that according to the values of both the objective function and the cluster validity indices, the performances of FCM algorithm often was improved by using the proposed hybrid approach. Also, it was evident from the results that in the case of minimizing the objective function, the combination of Ward's algorithm and FCM provided best results, but according to the cluster validity indices, other hybrid algorithms such as combinations of single linkage or complete linkage and FCM algorithm presented the most desirable results. In addition, according to the results, there are two well-defined homogeneous regions in Sefidroud basin identified by all the examined hybrid algorithms.

scholarly journals Boundary Matching and Interior Connectivity-Based Cluster Validity Anlysis

2020 ◽  
Vol 10 (4) ◽  
pp. 1337 ◽  
Author(s):  
Qi Li ◽  
Shihong Yue ◽  
Yaru Wang ◽  
Mingliang Ding ◽  
Jia Li ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Experimental Results ◽  
Cluster Validity ◽  
Validity Index ◽  
Boundary Points ◽  
Validity Indices ◽  
Boundary Matching ◽  
Interior Points

The evaluation of clustering results plays an important role in clustering analysis. However, the existing validity indices are limited to a specific clustering algorithm, clustering parameter, and assumption in practice. In this paper, we propose a novel validity index to solve the above problems based on two complementary measures: boundary points matching and interior points connectivity. Firstly, when any clustering algorithm is performed on a dataset, we extract all boundary points for the dataset and its partitioned clusters using a nonparametric metric. The measure of boundary points matching is computed. Secondly, the interior points connectivity of both the dataset and all the partitioned clusters are measured. The proposed validity index can evaluate different clustering results on the dataset obtained from different clustering algorithms, which cannot be evaluated by the existing validity indices at all. Experimental results demonstrate that the proposed validity index can evaluate clustering results obtained by using an arbitrary clustering algorithm and find the optimal clustering parameters.

Diesel engine injection faults' detection and classification utilizing unsupervised fuzzy clustering techniques

2019 ◽  
Vol 233 (16) ◽  
pp. 5622-5636
Author(s):  
Ezzeddine Ftoutou ◽  
Mnaouar Chouchane
Keyword(s):  
Diesel Engine ◽  
Fuzzy Clustering ◽  
Clustering Algorithms ◽  
Variable Number ◽  
High Detection Rate ◽  
Time Frequency ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Number Of Classes ◽  
Unsupervised Fuzzy Clustering

By using the unsupervised fuzzy clustering, this study attempts to design a new scheme for the unsupervised detection and classification of two injection faults using the time–frequency analysis of vibration signals of an internal combustion, four-stroke, diesel engine with six cylinders in-line. To reach this objective, two new methods called modified S-transform and two-dimensional non-negative matrix factorization are used. Three fuzzy clustering algorithms and nine cluster validity indices, for a variable number of classes, are also used to detect and classify the fault classes. The implementation of these methods resulted in a high detection rate of the injection faults.

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