Browsing by Subject "Adaptive algorithms"

Now showing 1 - 14 of 14

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Adaptive methods for dithering color images
(Institute of Electrical and Electronics Engineers, 1997-07) Akarun, L.; Yardımcı, Y.; Çetin, A. Enis
Most color image printing and display devices do not have the capability of reproducing true color images. A common remedy is the use of dithering techniques that take advantage of the lower sensitivity of the eye to spatial resolution and exchange higher color resolution with lower spatial resolution. In this paper, an adaptive error diffusion method for color images is presented. The error diffusion filter coefficients are updated by a normalized least mean square-type (LMS-type) algorithm to prevent textural contours, color impulses, and color shifts, which are among the most common side effects of the standard dithering algorithms. Another novelty of the new method is its vector character: Previous applications of error diffusion have treated the individual color components of an image separately. Here, we develop a general vector approach and demonstrate through simulation studies that superior results are achieved.
Open Access
Adaptive mixture methods based on Bregman divergences
(Elsevier, 2013) Donmez, M. A.; Inan, H. A.; Kozat, S. S.
We investigate adaptive mixture methods that linearly combine outputs of m constituent filters running in parallel to model a desired signal. We use Bregman divergences and obtain certain multiplicative updates to train the linear combination weights under an affine constraint or without any constraints. We use unnormalized relative entropy and relative entropy to define two different Bregman divergences that produce an unnormalized exponentiated gradient update and a normalized exponentiated gradient update on the mixture weights, respectively. We then carry out the mean and the mean-square transient analysis of these adaptive algorithms when they are used to combine outputs of m constituent filters. We illustrate the accuracy of our results and demonstrate the effectiveness of these updates for sparse mixture systems.
Unknown
Adaptive power control and MMSE interference suppression
(1998) Ulukus, S.; Yates, R.D.
Power control algorithms assume that the receiver structure is fixed and iteratively update the transmit powers of the users to provide acceptable quality of service while minimizing the total transmitter power. Multiuser detection, on the other hand, optimizes the receiver structure with the assumption that the users have fixed transmitter powers. In this study, we combine the two approaches and propose an iterative and distributed power control algorithm which iteratively updates the transmitter powers and receiver filter coefficients of the users. We show that the algorithm converges to a minimum power solution for the powers, and an MMSE multiuser detector for the filter coefficients.
Unknown
An adaptive, energy-aware and distributed fault-tolerant topology-control algorithm for heterogeneous wireless sensor networks
(Elsevier BV, 2016) Deniz, F.; Bagci, H.; Korpeoglu, I.; Yazıcı A.
This paper introduces an adaptive, energy-aware and distributed fault-tolerant topology-control algorithm, namely the Adaptive Disjoint Path Vector (ADPV) algorithm, for heterogeneous wireless sensor networks. In this heterogeneous model, we have resource-rich supernodes as well as ordinary sensor nodes that are supposed to be connected to the supernodes. Unlike the static alternative Disjoint Path Vector (DPV) algorithm, the focus of ADPV is to secure supernode connectivity in the presence of node failures, and ADPV achieves this goal by dynamically adjusting the sensor nodes' transmission powers. The ADPV algorithm involves two phases: a single initialization phase, which occurs at the beginning, and restoration phases, which are invoked each time the network's supernode connectivity is broken. Restoration phases utilize alternative routes that are computed at the initialization phase by the help of a novel optimization based on the well-known set-packing problem. Through extensive simulations, we demonstrate that ADPV is superior in preserving supernode connectivity. In particular, ADPV achieves this goal up to a failure of 95% of the sensor nodes; while the performance of DPV is limited to 5%. In turn, by our adaptive algorithm, we obtain a two-fold increase in supernode-connected lifetimes compared to DPV algorithm.
Unknown
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.
Unknown
Dynamic threshold-based assembly algorithms for optical burst switching networks subject to burst rate constraints
(Springer, 2010-04-17) Toksöz, M. A.; Akar, N.
Control plane load stems from burst control packets which need to be transmitted end-to-end over the control channel and furtherprocessed at core nodes of an optical burst switching (OBS) network for reserving resources in advance for an upcoming burst. Burst assembly algorithms are generally designed without taking into consideration the control plane load they lead to. In this study, we propose traffic-adaptive burst assembly algorithms that attempt to minimize the average burst assembly delay subject to burst rate constraints and hence limit the control plane load. The algorithms we propose are simple to implement and we show using synthetic and real traffic traces that they perform substantially better than the usual timer-based schemes.
Unknown
Entropy minimization based robust algorithm for adaptive networks
(IEEE, 2012) Köse, Kıvanç; Çetin, A. Enis; Gunay O.
In this paper, the problem of estimating the impulse responses of individual nodes in a network of nodes is dealt. It was shown by the previous work in literature that when the nodes can interact with each other, fusion based adaptive filtering approaches are more effective than handling nodes independently. Here we are proposing the use of entropy functional based optimization in the adaptive filtering stage. We tested the new method on networks under Gaussian and ε-contaminated Gaussian noise. The results show that the proposed method achieves significant improvements in the error rates in case of ε-contaminated noise. © 2012 IEEE.
Open Access
Improved convergence performance of adaptive algorithms through logarithmic cost
(IEEE, 2014-05) Sayın, Muhammed O.; Vanlı, N. Denizcan; Kozat, Süleyman S.
We present a novel family of adaptive filtering algorithms based on a relative logarithmic cost. The new family intrinsically combines the higher and lower order measures of the error into a single continuous update based on the error amount. We introduce the least mean logarithmic square (LMLS) algorithm that achieves comparable convergence performance with the least mean fourth (LMF) algorithm and overcomes the stability issues of the LMF algorithm. In addition, we introduce the least logarithmic absolute difference (LLAD) algorithm. The LLAD and least mean square (LMS) algorithms demonstrate similar convergence performance in impulse-free noise environments while the LLAD algorithm is robust against impulsive interference and outperforms the sign algorithm (SA). © 2014 IEEE.
Unknown
Model-free adaptive hysteresis for dynamic bandwidth reservation
(IEEE, 2007-10) Akar, Nail
Dynamic bandwidth reservation refers to the process of dynamically updating the bandwidth allocation to a connection between two network end points on the basis of actual aggregate traffic demand of the connection. We assume a scenario in which bandwidth updates for the connection should not be performed too frequently and the frequency of updates are thus limited to a so-called desired update rate. We propose an asynchronous model-free adaptive hysteresis algorithm for dynamic bandwidth reservations with such update frequency constraints. We validate the effectiveness of the proposed approach by comparing its bandwidth efficiency with that of a synchronous model-based dynamic bandwidth reservation mechanism from the existing literature.
Unknown
MPLS automatic bandwidth allocation via adaptive hysteresis
(Elsevier, 2010-11-29) Akar, N.; Toksöz, M. A.
MPLS automatic bandwidth allocation (or provisioning) refers to the process of dynamically updating the bandwidth allocation of a label switched path on the basis of actual aggregate traffic demand on this path. Since bandwidth updates require signaling, it is common to limit the rate of updates to reduce signaling costs. In this article, we propose a model-free asynchronous adaptive hysteresis algorithm for MPLS automatic bandwidth allocation under bandwidth update rate constraints. We validate the effectiveness of the proposed approach by comparing it against existing schemes in (i) voice and (ii) data traffic scenarios. The proposed method can also be used in more general GMPLS networks.
Unknown
QR-RLS algorithm for error diffusion of color images
(SPIE, 2000) Unal, G. B.; Yardimci, Y.; Arıkan, Orhan; Çetin, A. Enis
Printing color images on color printers and displaying them on computer monitors requires a significant reduction of physically distinct colors, which causes degradation in image quality. An efficient method to improve the display quality of a quantized image is error diffusion, which works by distributing the previous quantization errors to neighboring pixels, exploiting the eye's averaging of colors in the neighborhood of the point of interest. This creates the illusion of more colors. A new error diffusion method is presented in which the adaptive recursive least-squares (RLS) algorithm is used. This algorithm provides local optimization of the error diffusion filter along with smoothing of the filter coefficients in a neighborhood. To improve the performance, a diagonal scan is used in processing the image.
Unknown
Robust adaptive filtering algorithms for α-stable random processes
(Institute of Electrical and Electronics Engineers, 1999-02) Aydin, G.; Arıkan, Orhan; Çetin, A. Enis
A new class of algorithms based on the fractional lower order statistics is proposed for finite-impulse response adaptive filtering in the presence of α-stable processes. It is shown that the normalized least mean p-norm (NLMP) and Douglas' family of normalized least mean square algorithms are special cases of the proposed class of algorithms. A convergence proof for the new algorithm is given by showing that it performs a descent-type update of the NLMP cost function. Simulation studies indicate that the proposed algorithms provide superior performance in impulsive noise environments compared to the existing approaches.
Unknown
Robust least mean mixed norm adaptive filtering for α-stable random processes
(IEEE, 1997) Aydın, Gül; Tanrıkulu, O.; Çetin, A. Enis
Based on the concept of Fractional Lower Order Statistics (FLOS), we present the Robust Least Mean Mixed Norm (RLMMN) adaptive algorithm for applications in impulsive environments modeled by α-stable distributions. A sufficient condition for finite variance of the update term is obtained for the underlying α-stable process. Simulation results are provided regarding the identification of the parameters of an AR system.
Unknown
Wildfire detection using LMS based active learning
(IEEE, 2009-04) Töreyin, B. Uğur; Çetin, A. Enis
A computer vision based algorithm for wildfire detection is developed. The main detection algorithm is composed of four sub-algorithms detecting (i) slow moving objects, (ii) gray regions, (iii) rising regions, and (iv) shadows. Each algorithm yields its own decision as a real number in the range [-1,1] at every image frame of a video sequence. Decisions from subalgorithms are fused using an adaptive algorithm. In contrast to standard Weighted Majority Algorithm (WMA), weights are updated using the Least Mean Square (LMS) method in the training (learning) stage. The error function is defined as the difference between the overall decision of the main algorithm and the decision of an oracle, who is the security guard of the forest look-out tower. ©2009 IEEE.