analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving

, recognition-by-components (RBC), is that a modest set of generalized-cone components, called geons (N ^ 36), can be derived from contrasts of five readily detectable properties of edges in a two-dimensional image: curvature, collinearity, symmetry, parallelism, and cotermmation. The detection

repeatedly in an atomic block), (3) we use runtime filtering to detect duplicate log entries that are missed statically, and (4) we present a series of GC-time techniques to compact the logs generated by long-running atomic blocks. Our implementation supports short-running scalable concurrent benchmarks

We extend existing models for evaluating the reliability of detecting electronic watermarks in digital images. Probabilities of incorrect detections (missed detection and false alarms) are expressed in terms of the watermark-energy-to-image-variance power ratio. We present some counterintuitive

and can dynamically and autonomously adjust its operating parameters to access the spectrum. Distributed spectrum sensing (DSS) CR terminals form self-organizing networks capable to detect vacant spectrum bands that can be used for transmission Top priority: avoid harmful interference with primary users

Abstract — The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In the present work, it is shown that a missed detection in one part of the field of view has a significant effect

We describe a new technique for finding potential buffer overrun vulnerabilities in security-critical C code. The key to success is to use static analysis: we formulate detection of buffer overruns as an integer range analysis problem. One major advantage of static analysis is that security bugs

to reason about quantifiers. Instead of conventional matching in a term DAG, Simplify matches up to equivalence in an E-graph, which detects many relevant pattern instances that would be missed by the conventional approach. The paper describes two techniques, labels and counterexample contexts, for helping

under tracking. In this paper, we model the data association in VSN as an inference problem on dynamic Bayesian networks (DBN) and investigate the key problems for efficient data association in case of missing detection. Firstly, to deal with the problem of missing detection, we introduce a set

Abstract not found