266,090 research outputs found
A Soft Computing Approach to Dynamic Load Balancing in 3GPP LTE
A major objective of the 3GPP LTE standard is the provision of high-speed data services. These services must be guaranteed under varying radio propagation conditions, to stochastically distributed mobile users. A necessity for determining and regulating the traffic load of eNodeBs naturally ensues. Load balancing is a self-optimization operation of self-organizing networks (SON). It aims at ensuring an equitable distribution of users in the network. This translates into better user satisfaction and a more efficient use of network resources. Several methods for load balancing have been proposed. Most of the algorithms are based on hard (traditional) computing which does not utilize the tolerance for precision of load balancing. This paper proposes the use of soft computing, precisely adaptive Neuro-fuzzy inference system (ANFIS) model for dynamic QoS aware load balancing in 3GPP LTE. The use of ANFIS offers learning capability of neural network and knowledge representation of fuzzy logic for a load balancing solution that is cost effective and closer to human intuitio
Applications of Soft Computing in Mobile and Wireless Communications
Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications
Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database
In this paper we present a novel architecture for storing visual data.
Effective storing, browsing and searching collections of images is one of the
most important challenges of computer science. The design of architecture for
storing such data requires a set of tools and frameworks such as SQL database
management systems and service-oriented frameworks. The proposed solution is
based on a multi-layer architecture, which allows to replace any component
without recompilation of other components. The approach contains five
components, i.e. Model, Base Engine, Concrete Engine, CBIR service and
Presentation. They were based on two well-known design patterns: Dependency
Injection and Inverse of Control. For experimental purposes we implemented the
SURF local interest point detector as a feature extractor and -means
clustering as indexer. The presented architecture is intended for content-based
retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial
Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan
Multi-Thread Hydrodynamic Modeling of a Solar Flare
Past hydrodynamic simulations have been able to reproduce the high
temperatures and densities characteristic of solar flares. These simulations,
however, have not been able to account for the slow decay of the observed flare
emission or the absence of blueshifts in high spectral resolution line
profiles. Recent work has suggested that modeling a flare as an sequence of
independently heated threads instead of as a single loop may resolve the
discrepancies between the simulations and observations. In this paper we
present a method for computing multi-thread, time-dependent hydrodynamic
simulations of solar flares and apply it to observations of the Masuda flare of
1992 January 13. We show that it is possible to reproduce the temporal
evolution of high temperature thermal flare plasma observed with the
instruments on the \textit{GOES} and \textit{Yohkoh} satellites. The results
from these simulations suggest that the heating time-scale for a individual
thread is on the order of 200 s. Significantly shorter heating time scales (20
s) lead to very high temperatures and are inconsistent with the emission
observed by \textit{Yohkoh}.Comment: Submitted to Ap
Career Transitions and Trajectories: A Case Study in Computing
From artificial intelligence to network security to hardware design, it is
well-known that computing research drives many important technological and
societal advancements. However, less is known about the long-term career paths
of the people behind these innovations. What do their careers reveal about the
evolution of computing research? Which institutions were and are the most
important in this field, and for what reasons? Can insights into computing
career trajectories help predict employer retention?
In this paper we analyze several decades of post-PhD computing careers using
a large new dataset rich with professional information, and propose a versatile
career network model, R^3, that captures temporal career dynamics. With R^3 we
track important organizations in computing research history, analyze career
movement between industry, academia, and government, and build a powerful
predictive model for individual career transitions. Our study, the first of its
kind, is a starting point for understanding computing research careers, and may
inform employer recruitment and retention mechanisms at a time when the demand
for specialized computational expertise far exceeds supply.Comment: To appear in KDD 201
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
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