128,068 research outputs found
KFOA: K-mean clustering, Firefly based data rate Optimization and ACO routing for Congestion Control in WSN
Wireless sensor network (WSN) is assortment of sensor nodes proficient in environmental information sensing, refining it and transmitting it to base station in sovereign manner. The minute sensors communicate themselves to sense and monitor the environment. The main challenges are limited power, short communication range, low bandwidth and limited processing. The power source of these sensor nodes are the main hurdle in design of energy efficient network. The main objective of the proposed clustering and data transmission algorithm is to augment network performance by using swarm intelligence approach. This technique is based on K-mean based clustering, data rate optimization using firefly optimization algorithm and Ant colony optimization based data forwarding. The KFOA is divided in three parts: (1) Clustering of sensor nodes using K-mean technique and (2) data rate optimization for controlling congestion and (3) using shortest path for data transmission based on Ant colony optimization (ACO) technique. The performance is analyzed based on two scenarios as with rate optimization and without rate optimization. The first scenario consists of two operations as k- mean clustering and ACO based routing. The second scenario consists of three operations as mentioned in KFOA. The performance is evaluated in terms of throughput, packet delivery ratio, energy dissipation and residual energy analysis. The simulation results show improvement in performance by using with rate optimization technique
Global optimization algorithms for image registration and clustering
Global optimization is a classical problem of finding the minimum or maximum value of an objective function. It has applications in many areas, such as biological image analysis, chemistry, mechanical engineering, financial analysis, deep learning and image processing. For practical applications, it is important to understand the efficiency of global optimization algorithms. This dissertation develops and analyzes some new global optimization algorithms and applies them to practical problems, mainly for image registration and data clustering.
First, the dissertation presents a new global optimization algorithm which approximates the optimum using only function values. The basic idea is to use the points at which the function has been evaluated to decompose the domain into a collection of hyper-rectangles. At each step of the algorithm, it chooses a hyper-rectangle according to a certain criterion and the next function evaluation is at the center of the hyper-rectangle. The dissertation includes a proof that the algorithm converges to the global optimum as the number of function evaluations goes to infinity, and also establishes the convergence rate. Standard test functions are used to experimentally evaluate the algorithm.
The second part focuses on applying algorithms from the first part to solve some practical problems. Image processing tasks often require optimizing over some set of parameters. In the image registration problem, one attempts to determine the best transformation for aligning similar images. Such problems typically require minimizing a dissimilarity measure with multiple local minima. The dissertation describes a global optimization algorithm and applies it to the problem of identifying the best transformation for aligning two images.
Global optimization algorithms can also be applied to the data clustering problem. The basic purpose of clustering is to categorize data into different groups by their similarity. The objective cost functions for clustering usually are non-convex. -means is a popular algorithm which can find local optima quickly but may not obtain global optima. The different starting points for -means can output different local optima. This dissertation describes a global optimization algorithm for approximating the global minimum of the clustering problem.
The third part of the dissertation presents variations of the proposed algorithm that work with different assumptions on the available information, including a version that uses derivatives
Differential Spatio-temporal Multiband Satellite Image Clustering using K-means Optimization With Reinforcement Programming
Deforestration is one of the crucial issues in Indonesia because now Indonesia has world's highest deforestation rate. In other hand, multispectral image delivers a great source of data for studying spatial and temporal changeability of the environmental such as deforestration area. This research present differential image processing methods for detecting nature change of deforestration. Our differential image processing algorithms extract and indicating area automatically. The feature of our proposed idea produce extracted information from multiband satellite image and calculate the area of deforestration by years with calculating data using temporal dataset. Yet, multiband satellite image consists of big data size that were difficult to be handled for segmentation. Commonly, K- Means clustering is considered to be a powerfull clustering algorithm because of its ability to clustering big data. However K-Means has sensitivity of its first generated centroids, which could lead into a bad performance. In this paper we propose a new approach to optimize K-Means clustering using Reinforcement Programming in order to clustering multispectral image. We build a new mechanism for generating initial centroids by implementing exploration and exploitation knowledge from Reinforcement Programming. This optimization will lead a better result for K-means data cluster. We select multispectral image from Landsat 7 in past ten years in Medawai, Borneo, Indonesia, and apply two segmentation areas consist of deforestration land and forest field. We made series of experiments and compared the experimental results of K-means using Reinforcement Programming as optimizing initiate centroid and normal K-means without optimization process.Keywords: Deforestration, Multispectral images, landsat, automatic clustering, K-means
Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges
As a promising paradigm for fifth generation (5G) wireless communication
systems, cloud radio access networks (C-RANs) have been shown to reduce both
capital and operating expenditures, as well as to provide high spectral
efficiency (SE) and energy efficiency (EE). The fronthaul in such networks,
defined as the transmission link between a baseband unit (BBU) and a remote
radio head (RRH), requires high capacity, but is often constrained. This
article comprehensively surveys recent advances in fronthaul-constrained
C-RANs, including system architectures and key techniques. In particular, key
techniques for alleviating the impact of constrained fronthaul on SE/EE and
quality of service for users, including compression and quantization,
large-scale coordinated processing and clustering, and resource allocation
optimization, are discussed. Open issues in terms of software-defined
networking, network function virtualization, and partial centralization are
also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin
note: text overlap with arXiv:1407.3855 by other author
On Graph Stream Clustering with Side Information
Graph clustering becomes an important problem due to emerging applications
involving the web, social networks and bio-informatics. Recently, many such
applications generate data in the form of streams. Clustering massive, dynamic
graph streams is significantly challenging because of the complex structures of
graphs and computational difficulties of continuous data. Meanwhile, a large
volume of side information is associated with graphs, which can be of various
types. The examples include the properties of users in social network
activities, the meta attributes associated with web click graph streams and the
location information in mobile communication networks. Such attributes contain
extremely useful information and has the potential to improve the clustering
process, but are neglected by most recent graph stream mining techniques. In
this paper, we define a unified distance measure on both link structures and
side attributes for clustering. In addition, we propose a novel optimization
framework DMO, which can dynamically optimize the distance metric and make it
adapt to the newly received stream data. We further introduce a carefully
designed statistics SGS(C) which consume constant storage spaces with the
progression of streams. We demonstrate that the statistics maintained are
sufficient for the clustering process as well as the distance optimization and
can be scalable to massive graphs with side attributes. We will present
experiment results to show the advantages of the approach in graph stream
clustering with both links and side information over the baselines.Comment: Full version of SIAM SDM 2013 pape
A Dynamic Clustering and Resource Allocation Algorithm for Downlink CoMP Systems with Multiple Antenna UEs
Coordinated multi-point (CoMP) schemes have been widely studied in the recent
years to tackle the inter-cell interference. In practice, latency and
throughput constraints on the backhaul allow the organization of only small
clusters of base stations (BSs) where joint processing (JP) can be implemented.
In this work we focus on downlink CoMP-JP with multiple antenna user equipments
(UEs) and propose a novel dynamic clustering algorithm. The additional degrees
of freedom at the UE can be used to suppress the residual interference by using
an interference rejection combiner (IRC) and allow a multistream transmission.
In our proposal we first define a set of candidate clusters depending on
long-term channel conditions. Then, in each time block, we develop a resource
allocation scheme by jointly optimizing transmitter and receiver where: a)
within each candidate cluster a weighted sum rate is estimated and then b) a
set of clusters is scheduled in order to maximize the system weighted sum rate.
Numerical results show that much higher rates are achieved when UEs are
equipped with multiple antennas. Moreover, as this performance improvement is
mainly due to the IRC, the gain achieved by the proposed approach with respect
to the non-cooperative scheme decreases by increasing the number of UE
antennas.Comment: 27 pages, 8 figure
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