Browsing by Subject "Online systems"

Now showing 1 - 14 of 14

Open Access
Adaptive decision fusion based cooperative spectrum sensing for cognitive radio systems
(IEEE, 2011) Töreyin, B. U.; Yarkan, S.; Qaraqe, K. A.; Çetin, A. Enis
In this paper, an online Adaptive Decision Fusion (ADF) framework is proposed for the central spectrum awareness engine of a spectrum sensor network in Cognitive Radio (CR) systems. Online learning approaches are powerful tools for problems where drifts in concepts take place. Cooperative spectrum sensing in cognitive radio networks is such a problem where channel characteristics and utilization patterns change frequently. The importance of this problem stems from the requirement that secondary users must adjust their frequency utilization strategies in such a way that the communication performance of the primary users would not be degraded by any means. In the proposed framework, sensing values from several sensor nodes are fused together by weighted linear combination at the central spectrum awareness engine. The weights are updated on-line according to an active fusion method based on performing orthogonal projections onto convex sets describing power reading values from each sensor. The proposed adaptive fusion strategy for cooperative spectrum sensing can operate independent from the channel type between the primary user and secondary users. Results of simulations and experiments for the proposed method conducted in laboratory are also presented. © 2011 IEEE.
Open Access
Big-data streaming applications scheduling based on staged multi-armed bandits
(Institute of Electrical and Electronics Engineers, 2016) Kanoun, K.; Tekin, C.; Atienza, D.; Van Der Schaar, M.
Several techniques have been recently proposed to adapt Big-Data streaming applications to existing many core platforms. Among these techniques, online reinforcement learning methods have been proposed that learn how to adapt at run-time the throughput and resources allocated to the various streaming tasks depending on dynamically changing data stream characteristics and the desired applications performance (e.g., accuracy). However, most of state-of-the-art techniques consider only one single stream input in its application model input and assume that the system knows the amount of resources to allocate to each task to achieve a desired performance. To address these limitations, in this paper we propose a new systematic and efficient methodology and associated algorithms for online learning and energy-efficient scheduling of Big-Data streaming applications with multiple streams on many core systems with resource constraints. We formalize the problem of multi-stream scheduling as a staged decision problem in which the performance obtained for various resource allocations is unknown. The proposed scheduling methodology uses a novel class of online adaptive learning techniques which we refer to as staged multi-armed bandits (S-MAB). Our scheduler is able to learn online which processing method to assign to each stream and how to allocate its resources over time in order to maximize the performance on the fly, at run-time, without having access to any offline information. The proposed scheduler, applied on a face detection streaming application and without using any offline information, is able to achieve similar performance compared to an optimal semi-online solution that has full knowledge of the input stream where the differences in throughput, observed quality, resource usage and energy efficiency are less than 1, 0.3, 0.2 and 4 percent respectively.
Open Access
A comparison of epidemic algorithms in wireless sensor networks
(Elsevier BV, 2006-08-21) Akdere, M.; Bilgin, C. C.; Gerdaneri, O.; Korpeoglu, I.; Ulusoy, O.; Çetintemel, U.
We consider the problem of reliable data dissemination in the context of wireless sensor networks. For some application scenarios, reliable data dissemination to all nodes is necessary for propagating code updates, queries, and other sensitive information in wireless sensor networks. Epidemic algorithms are a natural approach for reliable distribution of information in such ad hoc, decentralized, and dynamic environments. In this paper we show the applicability of epidemic algorithms in the context of wireless sensor environments, and provide a comparative performance analysis of the three variants of epidemic algorithms in terms of message delivery rate, average message latency, and messaging overhead on the network. © 2006 Elsevier B.V. All rights reserved.
Open Access
Cryptographic solutions for genomic privacy
(Springer, 2016-02) Ayday, Erman
With the help of rapidly developing technology, DNA sequencing is becoming less expensive. As a consequence, the research in genomics has gained speed in paving the way to personalized (genomic) medicine, and geneticists need large collections of human genomes to further increase this speed. Furthermore, individuals are using their genomes to learn about their (genetic) predispositions to diseases, their ancestries, and even their (genetic) compatibilities with potential partners. This trend has also caused the launch of health-related websites and online social networks (OSNs), in which individuals share their genomic data (e.g., OpenSNP or 23andMe). On the other hand, genomic data carries much sensitive information about its owner. By analyzing the DNA of an individual, it is now possible to learn about his disease predispositions (e.g., for Alzheimer’s or Parkinson’s), ancestries, and physical attributes. The threat to genomic privacy is magnified by the fact that a person’s genome is correlated to his family members’ genomes, thus leading to interdependent privacy risks. In this work, focusing on our existing and ongoing work on genomic privacy, we will first highlight one serious threat for genomic privacy. Then, we will present the high level descriptions of our cryptographic solutions to protect the privacy of genomic data. © International Financial Cryptography Association 2016.
Open Access
Discriminative fine-grained mixing for adaptive compression of data streams
(Institute of Electrical and Electronics Engineers, 2014) Gedik, B.
This paper introduces an adaptive compression algorithm for transfer of data streams across operators in stream processing systems. The algorithm is adaptive in the sense that it can adjust the amount of compression applied based on the bandwidth, CPU, and workload availability. It is discriminative in the sense that it can judiciously apply partial compression by selecting a subset of attributes that can provide good reduction in the used bandwidth at a low cost. The algorithm relies on the significant differences that exist among stream attributes with respect to their relative sizes, compression ratios, compression costs, and their amenability to application of custom compressors. As part of this study, we present a modeling of uniform and discriminative mixing, and provide various greedy algorithms and associated metrics to locate an effective setting when model parameters are available at run-time. Furthermore, we provide online and adaptive algorithms for real-world systems in which system parameters that can be measured at run-time are limited. We present a detailed experimental study that illustrates the superiority of discriminative mixing over uniform mixing. © 2013 IEEE.
Open Access
An efficient virtual topology design and traffic engineering scheme for IP/WDM networks
(Springer, 2007) Şengezer, Namık; Karasan, Ezhan
We propose an online traffic engineering (TE) scheme for efficient routing of bandwidth guaranteed connections on a Multiprotocol label switching (MPLS)/wavelength division multiplexing (WDM) network with a traffic pattern varying with the time of day. We first consider the problem of designing the WDM virtual topology utilizing multi-hour statistical traffic pattern. After presenting an effective solution to this offline problem, we introduce a Dynamic tRaffic Engineering AlgorithM (DREAM) that makes use of the bandwidth update and rerouting of the label switched paths (LSPs). The performance of DREAM is compared with commonly used online TE schemes and it is shown to be superior in terms of blocked traffic ratio.
Open Access
An online adaptive cooperation scheme for spectrum sensing based on a second-order statistical method
(Institute of Electrical and Electronics Engineers, 2012) Yarkan S.; Töreyin, B. U.; Qaraqe, K. A.; Çetin, A. Enis
Spectrum sensing is one of the most important features of cognitive radio (CR) systems. Although spectrum sensing can be performed by a single CR, it is shown in the literature that cooperative techniques, including multiple CRs/sensors, improve the performance and reliability of spectrum sensing. Existing cooperation techniques usually assume a static communication scenario between the unknown source and sensors along with a fixed propagation environment class. In this paper, an online adaptive cooperation scheme is proposed for spectrum sensing to maintain the level of sensing reliability and performance under changing channel and environmental conditions. Each cooperating sensor analyzes second-order statistics of the received signal, which undergoes both correlated fast and slow fading. Autocorrelation estimation data from sensors are fused together by an adaptive weighted linear combination at the fusion center. Weight update operation is performed online through the use of orthogonal projection onto convex sets. Numerical results show that the performance of the proposed scheme is maintained for dynamically changing characteristics of the channel between an unknown source and sensors, even under different physical propagation environments. In addition, it is shown that the proposed cooperative scheme, which is based on second-order detectors, yields better results compared with the same fusion mechanism that is based on conventional energy detectors.
Open Access
Online bicriteria load balancing for distributed file servers
(IEEE, 2008-08) Tse, Savio
We study the online bicriteria load balancing problem in a system of M distributed homogeneous file servers located in a cluster. The load and storage space are assumed to be independent. We propose two online approximate algorithms for balancing the load and required storage space of each server during document placement. Our first algorithm combines the first result In [10] and the upper bound result In [1]. With applying document reallocation, we further obtain improvement and give a smoother tradeoff curve of the upper bounds of load and storage space. This result improves the best existing solutions. The second algorithm Is for theoretical purpose. Its existence proves that the bounds for the load and the required storage space of each server, respectively, are strictly better when document reallocation Is allowed. It enhances the research In applying document reallocation. The time complexities of both algorithms are O(log M); and the cost of document reallocation should be taken into account.
Open Access
An Online Causal Inference Framework for Modeling and Designing Systems Involving User Preferences: A State-Space Approach
(Hindawi Limited, 2017) Delibalta, I.; Baruh, L.; Kozat, S. S.
We provide a causal inference framework to model the effects of machine learning algorithms on user preferences. We then use this mathematical model to prove that the overall system can be tuned to alter those preferences in a desired manner. A user can be an online shopper or a social media user, exposed to digital interventions produced by machine learning algorithms. A user preference can be anything from inclination towards a product to a political party affiliation. Our framework uses a state-space model to represent user preferences as latent system parameters which can only be observed indirectly via online user actions such as a purchase activity or social media status updates, shares, blogs, or tweets. Based on these observations, machine learning algorithms produce digital interventions such as targeted advertisements or tweets. We model the effects of these interventions through a causal feedback loop, which alters the corresponding preferences of the user. We then introduce algorithms in order to estimate and later tune the user preferences to a particular desired form. We demonstrate the effectiveness of our algorithms through experiments in different scenarios. © 2017 Ibrahim Delibalta et al.
Open Access
Online detection of fire in video
(IEEE, 2007) Töreyin, Behçet Uğur; Çetin, A. Enis
This paper describes an online learning based method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. Our fire detection method consists of weak classifiers based on temporal and spatial modeling of flames. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish temporal flame flicker process from motion of flame colored moving objects. Boundary of flames are represented in wavelet domain and high frequency nature of the boundaries of fire regions is also used as a clue to model the flame flicker spatially. Results from temporal and spatial weak classifiers based on flame flicker and irregularity of the flame region boundaries are updated online to reach a final decision. False alarms due to ordinary and periodic motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.
Open Access
Online exercise ECG signal orthogonalization
(1996) Acar, B.; Köymen H.
In this paper an efficient method of making use of the redundancy in standard 12 lead ECG signals to eliminate noise is described. The method is based on orthogonalization via online Singular Value Decomposition (SVD). Its application as a filter to remove EMG noise and baseline wander are explained. A comparative study of ST analysis results of original and processed exercise ECG data is reported.
Open Access
Online solutions for scalable file server systems
(ACM, 2006) Tse, Savio S. H.
We propose three online algorithms for scalable file server systems. A scalable file server is expected to provide rather stable services while the numbers of users, tasks, and data volumes keep increasing. One of the purposes of parallel and distributed approaches is to achieve scalability. Sufficient hardware resources are essential for good services; however, a good coordination of them is also indispensable, as parallel and distributed resources need to complement the shortages of each other, and it falls on the shoulders of the algorithmic and architectural designs. In this paper, we address the load balancing problem in scalable file servers. The three online approximate algorithms proposed is for placing and deleting documents in a system of M distributed file servers located in a cluster in order to balance the loads and required storage spaces among all servers. In [7], we have proposed some algorithms without allowing re-allocation. In this paper, by paying the re-allocation cost, we have several improvements on some existing results. © 2006 ACM.
Open Access
PATIKAweb: a Web interface for analyzing biological pathways through advanced querying and visualization
(Oxford University Press, 2006-02-01) Doğrusöz, Uğur; Erson, E. Zeynep; Giral, Erhan; Demir, Emek; Babur, Özgün; Çetintaş, Ahmet; Çolak, Recep
Summary: PATIKAweb provides a Web interface for retrieving and analyzing biological pathways in the PATIKA database, which contains data integrated from various prominent public pathway databases. It features a user-friendly interface, dynamic visualization and automated layout, advanced graph-theoretic queries for extracting biologically important phenomena, local persistence capability and exporting facilities to various pathway exchange formats. © The Author 2005. Published by Oxford University Press. All rights reserved.
Open Access
Reactive scheduling in a dynamic and stochastic FMS environment
(Taylor & Francis, 2003) Sabuncuoğlu, İ.; Kızılışık, Ö. B.
In this paper, we study the reactive scheduling problems in a dynamic and stochastic manufacturing environment. Specifically, we develop a simulationbased scheduling system for flexible manufacturing systems. We also propose several reactive scheduling policies (i.e. when-to-schedule and how-to-schedule policies) and test their performances under various experimental conditions, processing time variations, and machine breakdowns. Moreover, we compare offline and online scheduling schemes in a dynamic manufacturing environment. The results of extensive simulation experiments indicate that the variable-timeresponse is better than the fixed-time-response. The full scheduling scheme generally performs better than the partial scheduling. Finally, the online scheduling is more robust to uncertainty and variations in processing times than the optimumseeking offline scheduling. A comprehensive bibliography is also provided in the paper.