Results 221 to 230 of about 6,996 (263)

Randomized Spectrum Transformations for Adapting Object Detector in Unseen Domains

IEEE Transactions on Image Processing, 2023
We propose a Meta Learning on Randomized Transformations (MLRT) to learn domain invariant object detectors. Domain generalization is a problem about learning an invariant model from multiple source domains which can generalize well on unseen target domains.
Lei Zhang 0038, Lingyun Qin, Mingjun Xu, Weijie Chen 0006, Shiliang Pu, Wensheng Zhang 0002   +5 more
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Randomized Approach to Nonlinear Inversion Combining Random and Optimized Simultaneous Sources and Detectors

SIAM Journal on Scientific Computing, 2019
Summary: In partial differential equation-based (PDE-based) inverse problems with many measurements, many large-scale discretized PDEs must be solved for each evaluation of the misfit or objective function. In the nonlinear case, evaluating the Jacobian requires solving an additional set of systems.
Selin S. Aslan, Eric de Sturler, Misha E. Kilmer   +2 more
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Random-Selection-Based Anomaly Detector for Hyperspectral Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2011
Anomaly detection in hyperspectral images is of great interest in the target detection domain since it requires no prior information and makes full use of the spectral differences revealed in hyperspectral images. The current anomaly detection methods are susceptible to anomalies in the processing window range or the image scope.
Bo Du 0001, Liangpei Zhang 0001
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Randomized SUSAN edge detector

Optical Engineering, 2011
A speed up technique for the SUSAN edge detec- tor based on random sampling is proposed. Instead of sliding the mask pixel by pixel on an image as the SUSAN edge detector does, the proposed scheme places the mask ran- domly on pixels to find edges in the image; we hereby name it randomized SUSAN edge detector (R-SUSAN).
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A Modified Energy Detector for Random Signals in Gaussian Noise

IEEE Communications Letters, 2015
This letter proposes a new modified energy detector for random signals in Gaussian noise. This new detector, whose decision variable is the weighted sum of the signal amplitude and its square, has a simple structure similar to the conventional energy detector.
Huayan Guo, Wei Jiang 0003, Wu Luo
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Randomized Low-Rank Nonlinear RX Detector

2023
Anomaly Detection is an important topic in various application areas, including image analysis and network intrusion detection. The Reed–Xiaoli (RX) detector is an efficient and accurate anomaly detector that can be used if analyzed data is Gaussian distributed. However, in the real-world, data is rarely Gaussian distributed. For nonlinear data, kernel
Selçuk Yapıcı, Fatih Nar
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Performance analysis of a detector for nonstationary random signals

1995 International Conference on Acoustics, Speech, and Signal Processing, 2002
The detection of nonstationary random signals is an important sonar problem which also has potential applications in diverse areas such as biomedical signal processing and spread spectrum communications. The primary problem with applying a powerful test like the generalized likelihood ratio test (GLRT) is the computational effort required to search for
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An asymptotically optimal random modem and detector for robust communication

IEEE Transactions on Information Theory, 1990
Coherent communication over a waveform channel corrupted by thermal noise and by an unknown and arbitrary interfering signal of bounded power is considered. For a fixed encoder, a random modulator/demodulator (modem) and detector are derived. They asymptotically minimize the worst-case error probability as the blocklength of the encoder becomes large ...
Brian L. Hughes, Murad Hizlan
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A Collision Resolution Protocol for Random Access Channels with Energy Detectors

IEEE Transactions on Communications, 1982
In this paper, we consider the random accessing of a single slotted channel by a large number of packet-transmitting, bursty users. We assume that feedback broadcasting is available where some different information, in addition to the information assumed by the Capetanakis, Gallager, Massey, etc., models, is included in the feedback.
Leonidas Georgiadis, P. Papantoni-Kazakos   +1 more
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Locally robust array detectors for random signals

IEEE Transactions on Information Theory, 1978
Summary: Detection of random signals in arrays of receivers is considered when the exact distribution of the additive noise is not known. Locally robust correlator-type test statistics are obtained for weak-signal detection, for multivariate array noise densities in classes described by three different extensions of the univariate Tukey-Huber ...
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