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

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 recently introduced theory of compressive sensing enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist-rate samples. Interestingly, it has been shown that random projections are a near-optimal measurement scheme. This has inspired the design of hardware systems that directly implement random measurement protocols. However, despite the intense focus of the community on signal recovery, many (if not most) signal processing problems do not require full signal recovery. In this paper, we take some first steps in the direction of solving inference problems—such as detection, classification, or estimation—and filtering problems using only compressive measurements and without ever reconstructing the signals involved. We provide theoretical bounds along with experimental results.

A surge of development of new public health surveillance systems designed to provide more timely detection of outbreaks suggests that public health has a new requirement: extreme timeliness of detection. The authors review previous work relevant to measuring timeliness and to defining timeliness requirements. Using signal detection theory and decision theory, the authors identify strategies to improve timeliness of detection and position ongoing system development within that framework. Key words: decision theory, detection, epidemiology, population surveillance, public health

Abstract—This paper combines wavelet transforms with basic detection theory to develop a new unsupervised method for robustly detecting and localizing spikes in noisy neural recordings. The method does not require the construction of templates, or the supervised setting of thresholds. We present extensive Monte Carlo simulations, based on actual extracellular recordings, to show that this technique surpasses other commonly used methods in a wide variety of recording conditions. We further demonstrate that falsely detected spikes corresponding to our method resemble actual spikes more than the false positives of other techniques such as amplitude thresholding. Moreover, the simplicity of the method allows for nearly real-time execution. Index Terms—Arrival time estimation, continuous wavelet transform, unsupervised spike detection. I.

The goal of this paper is to highlight the significance of the timing acquisition problem in ultra-wideband (UWB) communication systems and discuss efficient solutions to the problem. We discuss how the distinguishing features of UWB communication systems, such as their wide bandwidth and low transmission power constraints, are responsible for making the acquisition of UWB signals a difficult task. A survey of the current approaches to UWB signal acquisition is also given. In addition, we discuss some of the issues and challenges in UWB signal acquisition which may not have received sufficient attention in existing literature.

Motivated by the goals of improving detection of low-contrast and narrow vessels and eliminating false detections at non-vascular structures, a new technique is presented for extracting vessels in retinal images. The core of the technique is a new likelihood ratio test that combines matchedfilter responses, confidence measures and vessel boundary measures. Matched filter responses are derived in scale-space to extract vessels of widely varying widths. A vessel confidence measure is defined as a projection of a vector formed from a normalized pixel neighborhood onto a normalized ideal vessel profile. Vessel boundary measures and associated confidences are computed at potential vessel boundaries. Combined, these responses form a 6-dimensional measurement vector at each pixel. A training technique is used to develop a mapping of this vector to a likelihood ratio that measures the "vesselness" at each pixel. Results comparing this vesselness measure to matched filters alone and to measures based on the Hessian of intensities show substantial improvements both qualitatively and quantitatively. The Hessian can be used in place of the matched filter to obtain similar but less-substantial improvements or to steer the matched filter by preselecting kernel orientations. Finally, the new vesselness likelihood ratio is embedded into a vessel tracing framework, resulting in an e#cient and e#ective vessel centerline extraction algorithm.

Despite the seemingly irreducible simplicity of Witsenhausen’s counterexample, the optimal control law for the problem is as yet unknown. It has been observed that the problem contains an implicit communication channel, which motivates formulating a vector version of the Witsenhausen counterexample that simplifies the problem in the limit of large vector lengths. This vector version can be viewed as a toy wireless communication problem that we call “Assisted Interference Suppression. ” A new information-theoretic lower bound on the average costs for this problem is derived. Then, using concepts of lossy compression, channel coding, and dirty-paper coding, nonlinear control strategies are developed that attain costs within a uniformly bounded constant factor of the optimal cost for this vector problem in the limit of infinite vector length. This is demonstrated by showing that the ratio of the upper and lower bounds is no more than 2 regardless of the problem parameters. Restricted to the scalar problem, it is shown that the new lower bound can be better than Witsenhausen’s bound by an arbitrarily large factor.

A noisy input signal is observed by means of a parallel array of one-bit threshold quantizers, in which all the quantizer outputs are added to produce the array output. This parsimonious signal representation is used to implement an optimal detection from the output of the array. Such conditions can be relevant for fast real-time processing in largescale sensor networks. We demonstrate that, even for suprathreshold input signals, the presence of independent noises added to the thresholds in the array, can lead to a better performance in the optimal detection. We relate these results to the phenomenon of suprathreshold stochastic resonance, by which nonlinear transmission or processing of signals with arbitrary amplitude can be improved by added noises in arrays.

The quality of information (QoI) that sensor networks provide to the applications they support is an important design goal for their deployment and use. In this paper, we introduce a layered framework for QoI-centered evaluation of sensor network deployment. The layered framework allows decomposing the deployment evaluation in three steps: input pre-processing, core analysis, and result post-processing. The layering allows the creation of a rich, modular toolkit for QoI-centered analysis that can accommodate both existing and new system modeling and analysis techniques. We demonstrate the utility of the framework by comparing the QoI performance of finite-sized sensor networks with general deployment topology. We also derive some new analysis results for the class of applications considered herein. 1.