Dietmar Kunz

Channel assignment for cellular radio using simulated annealing

The channel assignment problem, i.e. the task of assigning the channels to the radio base stations in a spectrum-efficient way, is an NP-complete optimization problem occurring during design of cellular radio systems. Previously, this problem has been solved by graph coloring algorithms. An alternative approach is presented. The problem is solved using simulated annealing, which is a general approach to combinatorial optimization. The algorithm has been successfully applied to practical radio network planning situations. One major benefit of the approach consists in the enhanced flexibility it gives to the engineer. >

Channel assignment for cellular radio using neural networks

The channel assignment problem, i.e. the task of assigning channels to radio cells in a spectrum-efficient way, is solved by a neural network algorithm. This algorithm is inherently parallel and does not rely on a particular structure of the interference graph. The results obtained so far indicate that the algorithm can be used to obtain an optimum solution. It was applied successfully for inhomogeneous interference conditions and channel demand. Cochannel and cosite constraints were taken into account, and the extension to any other technical restrictions will be possible in an obvious way. The examples studied to date are of a relatively small size; the question remains of how the algorithm behaves when applied to larger and more complex examples. The disadvantages of the algorithm are its long calculation time compared to graph coloring algorithms and the difficulty of finding appropriate parameters. However, the algorithm was not optimized for speed, and the parameter search may be a question of experience. >

Anisotropic spectral magnitude estimation filters for noise reduction and image enhancement

Describes an algorithm for noise reduction and enhancement of images which is able to take into account anisotropies of signal as well as of noise. Processing is based on subjecting each image to a block DFT, followed by comparing each observed magnitude coefficient to the expected noise standard deviation for it. Depending on this comparison, each coefficient is attenuated the more, the more likely it is that it contains only noise. In addition, the attenuation is made dependent on whether or not the observed coefficient contributes to an oriented prominent structure within the processed image block. Orientation as well as the distinctness with which it occurs are detected in the spectral domain by an inertia-like matrix. Orientation information is additionally exploited to selectively enhance oriented structures, thus only marginally increasing noise as compared to isotropic enhancement.

Nonlinear multiresolution gradient adaptive filter for medical images

We present a novel method for intra-frame image processing, which is applicable to a wide variety of medical imaging modalities, like X-ray angiography, X-ray fluoroscopy, magnetic resonance, or ultrasound. The method allows to reduce noise significantly - by about 4.5 dB and more - while preserving sharp image details. Moreover, selective amplification of image details is possible. The algorithm is based on a multi-resolution approach. Noise reduction is achieved by non-linear adaptive filtering of the individual band pass layers of the multi-resolution pyramid. The adaptivity is controlled by image gradients calculated from the next coarser layer of the multi-resolution pyramid representation, thus exploiting cross-scale dependencies. At sites with strong gradients, filtering is performed only perpendicular to the gradient, i.e. along edges or lines. The multi-resolution approach processes each detail on its appropriate scale so that also for low frequency noise small filter kernels are applied, thus limiting computational costs and allowing a real-time implementation on standard hardware. In addition, gradient norms are used to distinguish smoothly between “structure” and “noise only” areas, and to perform additional noise reduction and edge enhancement by selectively attenuating or amplifying the corresponding band pass coefficients.

Bayesian motion estimation for temporally recursive noise reduction in X-ray fluoroscopy

Abstract This paper develops a Bayesian motion estimation algorithm for motion-compensated temporally recursive filtering of moving low-dose X-ray images (X-ray fluoroscopy). These images often exhibit a very low signal-to-noise ratio. The described motion estimation algorithm is made robust against noise by spatial and temporal regularization. A priori expectations about the spatial and temporal smoothness of the motion vector field are expressed by a generalized Gauss-Markov random field. The advantage of using a generalized Gauss-Markov random field is that, apart from smoothness, it also captures motion edges without requiring an edge detection threshold. The costs of edges are controlled by a single parameter, by means of which the influence of the regularization can be tuned from a median-filter-like behaviour to a linear-filter-like one.

A lapped directional transform for spectral image analysis and its application to restoration and enhancement

Abstract We describe a new real-valued lapped transform for 2D-signal and image processing. Lapped transforms are particularly useful in block-based processing, since their overlapping basis functions reduce or prevent block artifacts. Our transform is derived from the modulated lapped transform (MLT), which is a real-valued and separable transform. Like the discrete cosine transform, the MLT does not allow to unambiguously identify spatial orientation from modulus spectra or spectral energy. This is in marked contrast to the complex-valued discrete Fourier transform (DFT). The new lapped transform is real valued, and at the same time allows unambiguous detection of spatial orientation from spectral energy. Furthermore, a fast and separable algorithm for this transform exists. As an application example, we investigate the transforms performance in anisotropic spectral approaches to image restoration and enhancement, and compare it to the DFT.

Multiscale linear/median hybrid filters for noise reduction in low dose X-ray images

This contribution describes a new filtering technique for the reduction of noise in medical X-ray images acquired with very low doses, like fluoroscopy images. In such images, only between 10 and 200 X-ray quanta contribute to each pixel. The resulting Poisson statistic causes a strong deterioration of image quality by quantum noise, which, in the observed images, is signal-dependent and exhibits a lowpass-shaped power spectrum. The central part of our approach is a linear/median hybrid filter, which is well-known for its good detail preservation properties. Noise reduction by this filter, however, depends on the presence or absence of underlying signal slopes, and is limited to a small range of spatial frequencies. Also, like median filters this filter tends to generate streaking artifacts. We show how a combination of linear/median hybrid filtering with a multiscale pyramid avoids these shortcomings, while simultaneously improving noise reduction performance substantially.

Lapped directional transform: a new transform for spectral image analysis

We propose a new real-valued lapped transform for 2D-signal and image processing. Lapped transforms are particularly useful in block-based processing, since their intrinsically overlapping basis functions reduce or prevent block artifacts. Our transform is derived from the modulated lapped transform (MLT), which, as a real-valued and separable transform like the discrete cosine transform, does not allow to unambiguously identify oriented structures from modulus spectra. This is in marked contrast to the (complex-valued) discrete Fourier transform (DFT). The new lapped transform is real-valued, and at the same time allows unambiguous detection of spatial orientation. Furthermore, a fast algorithm for this transform exists. As an application example, we investigate the transforms performance in spectral approaches to image restoration and enhancement in comparison to the DFT.

Static and Dynamic Channel Assignment Using Simulated Annealing

When planning a radio network, the operator has to assign frequencies, or more general channels, to base stations in, such a way that both, the call quality and the channel availability are good enough. This means that for an incoming call request the probability that an idle channel can be found is sufficiently high, and the probability that the signal-to-interference-ratio S/I falls short of a predefined value is sufficiently low.

Practical channel assignment using neural networks

The channel assignment problem (CAP) i.e. the task of assigning the channels to the radio base stations, is an NP-complete optimization problem occurring during design of cellular radio systems. An investigation is carried out to see how far a parallel neural network algorithm may be used for a real-world CAP. The neural network algorithm identified a channel assignment without heuristic rules generating the sequence of channel assignments. Formulating the optimization problems in terms of an energy function turned out to be very flexible, making it easy to include a variety of additional technical constraints and optimization criteria. Since a distinction between hard constraints and soft optimization criteria is only expressed by the relative strength of the terms in the energy function, the user is free to choose this relative strength according to his actual planning situation. Therefore, this neural network algorithm is well suited for the solution of real-world channel assignment problems.<<ETX>>