Kung Yao · 2004
2. Wireless links in sensor networks 3. Wireless fading channel characterization 4. A unified model for wireless fading statistics based on SIRP/Fox H-functions 5. Random media modeling 6. Conclusions – theoretical and practical system implications 31. Wireless sensor network performance • Packet loss is a basic measure of performance of a packet network (e.g., sensor network) • Packet loss depends on many layers in the OSI • We consider only the wireless links of the physical layer that use low-power radios in hostile fading propagation media • In a wireless sensor network, packet loss can not to analyzed or predicted without a basic understanding of the radio propagation issue
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Frits Agterberg · Encyclopedia of earth sciences series/Encyclopedia of earth sciences · 2021
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2015
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Erik G. Larsson, Petre Stoica · Cambridge University Press eBooks · 2003
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Martin H. Weik · 2000
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2013
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Hamed Aghaei (11060633), Mostafa Mirzaei Aliabadi (11060636), Farzaneh Mollabahrami (11060639) et al. · Figshare · 2021
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Nelson H. F. Beebe · 2017
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Shahabeddin Sotudian (14885933), Ruidi Chen (14885936), Ioannis Ch. Paschalidis (376482) · Figshare · 2023
<div><p>Despite their satisfactory performance, most existing listwise <i>Learning-To-Rank (LTR)</i> models do not consider the crucial issue of robustness. A data set can be contaminated in various ways, including human error in labeling or annotation, distributional data shift, and malicious adversaries who wish to degrade the algorithm’s performance. It has been shown that <i>Distributionally Robust Optimization (DRO)</i> is resilient against various types of noise and perturbations. To fill this gap, we introduce a new listwise LTR model called <i>Distributionally Robust Multi-output Regression Ranking (DRMRR)</i>. Different from existing methods, the scoring function of DRMRR was designed as a multivariate mapping from a feature vector to a vector of deviation scores, which captures local context information and cross-document interactions. In this way, we are able to incorporate the LTR metrics into our model. DRMRR uses a Wasserstein DRO framework to minimize a multi-output loss function under the most adverse distributions in the neighborhood of the empirical data distribution defined by a Wasserstein ball. We present a compact and computationally solvable reformulation of the min-max formulation of DRMRR. Our experiments were conducted on two real-world applications: medical document retrieval and drug response prediction, showing that DRMRR notably outperforms state-of-the-art LTR models. We also conducted an extensive analysis to examine the resilience of DRMRR against various types of noise: Gaussian noise, adversarial perturbations, and label poisoning. Accordingly, DRMRR is not only able to achieve significantly better performance than other baselines, but it can maintain a relatively stable performance as more noise is added to the data.</p></div>
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2016
A lot has been written about coincidence, including research into the probabilistic, methodological, psychological and philosophical aspects of coincidence This paper presents some aspects of the role of coincidence in the human cognitive system, focusing in particular on probabilistic reasoning and logical and methodological errors in reasoning, associated with the perception and interpretation of coincidence.Due to the nature of things, it is not possible to omit the areas of research mentioned above.
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R. A. Rojas, E. Banta · IEEE Transactions on Communication Technology · 1964
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D. Lao, Alexander M. Haimovich · IEEE Transactions on Communications · 2004
New expressions are derived for the exact symbol error probability and bit-error probability for optimum combining with multiple phase-shift keying. The expressions are for any numbers of equal-power cochannel interferers and receive branches. It is assumed that the aggregate interference and noise is Gaussian and that both the desired signal and interference are subject to flat Rayleigh fading. The new expressions have low computational complexity, as they contain only a single integral form with finite limits and finite integrand.
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Fmj Frans Willems, Alija Pašíć · TU/e Research Portal (Eindhoven University of Technology) · 1996
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Andrea Di Gioacchino (13884114), Jonah Procyk (13268318), Marco Molari (13884115) et al. · Figshare · 2022
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Siu‐Wai Ho, Sergio Verdú · 2008
Fano's inequality relates the error probability and conditional entropy of a finitely-valued random variable X given another random variable Y. It is not necessarily tight when the marginal distribution of X is fixed. In this paper, we consider both finite and countably infinite alphabets. A tight upper bound on the conditional entropy of X given Y is given in terms of the error probability and the marginal distribution of X. A new lower bound on the conditional entropy for countably infinite alphabet is also found. The equivalence of the reliability criteria of vanishing error probability and vanishing conditional entropy is established in wide generality.
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Marco Baldi, Franco Chiaraluce, Kløve Fellow Ieee · 2007
Computation of the undetected error probability for error correcting codes over the Z-channel is an important issue, explored only in part in previous literature. In this paper we consider the case of Varshamov-Tenengol’ts codes, by presenting some analytical, numerical, and heuristic methods for unveiling this additional feature. Possible comparisons with Hamming codes are also shown and discussed.
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Wayne Stark · Cambridge University Press eBooks · 2023
Master the fundamentals of digital communications systems with this accessible and hands-on introductory textbook, carefully interweaving theory and practice. The just-in-time approach introduces essential background as needed, keeping academic theory firmly linked to practical applications. The example-led teaching frames key concepts in the context of real-world systems, such as 5G, WiFi, and GPS. Stark provides foundational material on the trade-offs between energy and bandwidth efficiency, giving students a solid grounding in the fundamental challenges of designing digital communications systems. Features include over 300 illustrative figures, 80 examples, and 130 end-of-chapter problems to reinforce student understanding, with solutions for instructors. Accompanied online by lecture slides, computational MATLAB® and Python resources, and supporting data sets, this is the ideal introduction to digital communications for senior undergraduate and graduate students in electrical engineering.
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Andrzej Pelc · Theoretical Computer Science · 1989
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Luigi Paura · 2011
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Nelson M. Blachman · IRE Transactions on Communications Systems · 1981
Expressions are found for the effect of an error in the delay of the preceding signal, which provides the reference phase for the decoding of the present signal in differential phase-shift-keying reception. The signal-to-noise ratio is allowed to be different for the two signals that are compared by the receiver's phase detector. The results are applicable to both binary and quaternary DPSK. In addition, an approximation is obtained for the error probability when the two Signal-to-noise ratios are equal to the same large value.
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