This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Researchin shape analysis has been motivated, in part, by studies of human visual form perception systems.

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Since the invention of \turbo codes" by Berrou et al. in 1993, the \turbo principle" has been adapted to several communication problems such as \turbo equalization", \turbo trellis coded modulation", and iterative multi user detection. In this paper we study the \turbo equalization" approach, which can be applied to coded data transmission over channels with intersymbol interference (ISI). In the original system invented by Douillard et al., the data is protected by a convolutional code and a receiver consisting of two trellis-based detectors are used, one for the channel (the equalizer) and one for the code (the decoder). It has been shown that iterating equalization and decoding tasks can yield tremendous improvements in bit error rate (BER). We introduce new approaches to combining equalization based on linear ltering with the decoding. The result is a receiver that is capable of improving BER performance through iterations of equalization and decoding in a manner similar to turbo ...

Wireless networks are built upon a shared medium that makes it easy for adversaries to launch jamming-style attacks. These attacks can be easily accomplished by an adversary emitting radio frequency signals that do not follow an underlying MAC protocol. Jamming attacks can severely interfere with the normal operation of wireless networks and, consequently, mechanisms are needed that can cope with jamming attacks. In this paper, we examine radio interference attacks from both sides of the issue: first, we study the problem of conducting radio interference attacks on wireless networks, and second we examine the critical issue of diagnosing the presence of jamming attacks. Specifically, we propose four different jamming attack models that can be used by an adversary to disable the operation of a wireless network, and evaluate their effectiveness in terms of how

Abstract — This paper deals with arbitrarily distributed finitepower input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the inputoutput mutual information and the minimum mean-square error (MMSE) achievable by optimal estimation of the input given the output. That is, the derivative of the mutual information (nats) with respect to the signal-to-noise ratio (SNR) is equal to half the MMSE, regardless of the input statistics. This relationship holds for both scalar and vector signals, as well as for discrete-time and continuous-time noncausal MMSE estimation. This fundamental information-theoretic result has an unexpected consequence in continuous-time nonlinear estimation: For any input signal with finite power, the causal filtering MMSE achieved at SNR is equal to the average value of the noncausal smoothing MMSE achieved with a channel whose signal-to-noise ratio is chosen uniformly distributed between 0 and SNR. Index Terms — Mutual information, Gaussian channel, minimum mean-square error (MMSE), Wiener process, optimal

We outline a framework for collaborative signal processing in distributed sensor networks. The ideas are presented in the context of tracking multiple moving objects in a sensor field. The key steps involved in the tracking procedure include event detection, target classification, and estimation and prediction of target location. Algorithms for various tasks are discussed with an emphasis on classification. Results based on experiments with real data are reported which provide useful insights into the essential nature of the problems. Issues, challenges and directions for future research are identified.

Randomization has emerged as a useful technique for data disguising in privacy-preserving data mining. Its privacy properties have been studied in a number of papers. Kargupta et al. challenged the randomization schemes, and they pointed out that randomization might not be able to preserve privacy. However, it is still unclear what factors cause such a security breach, how they affect the privacy preserving property of the randomization, and what kinds of data have higher risk of disclosing their private contents even though they are randomized. We believe that the key factor is the correlations among attributes. We propose two data reconstruction methods that are based on data correlations. One method uses the Principal Component Analysis (PCA) technique, and the other method uses the Bayes Estimate (BE) technique. We have conducted theoretical and experimental analysis on the relationship between data correlations and the amount of private information that can be disclosed based our proposed data reconstructions schemes. Our studies have shown that when the correlations are high, the original data can be reconstructed more accurately, i.e., more private information can be disclosed. To improve privacy, we propose a modified randomization scheme, in which we let the correlation of random noises “similar ” to the original data. Our results have shown that the reconstruction accuracy of both PCA-based and BEbased schemes become worse as the similarity increases.

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.

The idea of using knowledge of the current channel fading values to optimize the transmitted signal in wireless communication systems has attracted substantial research attention in recent years. However, the practicality of this adaptive signaling has been questioned due to the variation of the wireless channel over time, which results in a different channel at the time of data transmission than at the time of channel estimation. By characterizing the effects of fading channel variation on the adaptive signaling paradigm, it is demonstrated here that these misgivings are well founded, as the channel variation greatly alters the nature of the problem. The main goal of this paper is to employ this characterization of the effects of the channel variation to design adaptive signaling schemes that are effective for the time-varying channel. The design of uncoded adaptive quadrature amplitude modulation (QAM) systems is considered first, and it demonstrates the need to consider the channel variation in system design. This is followed by the main contribution of this paper; using only a single outdated fading estimate when neither the Doppler frequency nor the exact shape of the autocorrelation function of the channel fading process is known, adaptive trellis-coded modulation schemes are designed that can provide a significant increase in bandwidth efficiency over their nonadaptive counterparts on time-varying channels.