KEYWORDS: Data modeling, Data communications, Data compression, Analog electronics, Binary data, Systems modeling, Signal to noise ratio, Electronics engineering, Telecommunications, Statistical analysis
Turbo codes have achieved near Shannon limit performance in data communication over noisy channels. Recently introduced Extrinsic Information Transfer (EXIT) Charts [1] have become an essential part of turbo code design and have also been used as a complementary design tool for the traditional bit error rate simulations. Additionally, compressive turbo codes have been shown to achieve near-entropy performance in different source coding problems [2], [3], [4]. The main objective of this paper is an extension of EXIT charts from turbo channel codes to turbo source codes, as well as extension of this technique to analog and finite precision iterative decoders.
KEYWORDS: Standards development, Antennas, Systems modeling, Signal to noise ratio, Data modeling, Receivers, Wireless communications, Orthogonal frequency division multiplexing, Visual process modeling, Electronics engineering
A user-scheduling algorithm for MU-MIMO systems is presented in this paper. The algorithm is a codebook based precoding method which can be suitable for the IEEE 802.16m mobile broadband standard. The proposed algorithm can effectively improve the sum capacity and fairness among the users.
In this paper, an indoor localization method based on Kalman filtered RSSI is presented. The indoor communications environment however is rather harsh to the mobiles since there is a substantial number of objects distorting the RSSI signals; fading and interference are main sources of the distortion. In this paper, a Kalman filter is adopted to filter the RSSI signals and the trilateration method is applied to obtain the robust and accurate coordinates of the mobile station. From the indoor experiments using the WiFi stations, we have found that the proposed algorithm can provide a higher accuracy with relatively lower power consumption in comparison to a conventional method.
A snake-based algorithm for segmenting an object from a pair of stereo images is presented. Unlike previously developed snake-based algorithms, this one performs well even when the objects in the picture are occluded and the background behind them is cluttered. Also, the algorithm is not sensitive to the placement of the initial snake points. The algorithm uses a new energy function defined over the disparity space between the pair of the stereo images to successfully locate the boundary of an object in a complex image. Experimental results have shown that the developed algorithm produces more accurate segmentation results than those of the well-known conventional snake algorithm reported by Kass et al.
KEYWORDS: Motion estimation, Image filtering, 3D image processing, Filtering (signal processing), Optical filters, Signal to noise ratio, Electronic filtering, Image analysis, 3D modeling, Linear filtering
A new computational approach for determining the parameters that characterize the locations of trajectories of point targets in a 3-D space is described. The targets of concern are dim, unresolved point targets moving along straight paths across the same field of view. Since the target's signal-to-noise ratio is low and the spatial extent of the target is less than a pixel, one must rely on integration over a target track that spans many image frames. The proposed method estimates these parameters by transforming the entire set of time-sequential images of a constant field of view into the projection space by using a modified Radon transform. Since the 3-D (spatiotemporal) data can be decomposed into 2-D multiple-view representations along arbitrary orientations, the Radon transform enables us to analyze the 3-D problem in terms of its 2-D projections. When this generalization of the Hough transform-based algorithm using the Radon transform is applied to a set of real infrared images, it produces promising estimation results even under noisy conditions. The noise in the images is assumed to be additive white Gaussian.
The conventional two-dimensional Hough transform technique is generalized into a projection- based transform method by using the modified Radon transform for estimating a three- dimensional target tracks embedded in a time-sequential set of image frames. The target of concern are dim, unresolved point targets moving along straight paths across a same field of view. Since the target signal-to-noise is low and the spatial extend of the target is less than a pixel, one must rely on integration over a target track which span over many image frames. Instead of processing the entire 3-D data set, a set of projections are taken using the modified 3-D Radon transform. The projection frames are processed further to extract the track parameters using the Hough transform. This projection-based method not only lowers the data dimensionality but maintains a comparable estimation performance to that of using the entire 3-D data by successfully incorporating all available knowledge obtained from the set of projections. The simulation results are presented for the synthetic and real infrared image sequences containing synthetically generated 3-D target tracks under various signal-to-noise conditions.
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