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Aggregation Rate for Compressible Functions
Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2017One of the most fundamental tasks in sensor networks is the computation of a (compressible) aggregation function of the input measurements. What rate of computation can be maintained, by properly choosing the aggregation tree, the TDMA schedule of the tree edges, and the transmission powers? We show here that the optimal rate is effectively a constant.
Magnús M. Halldórsson, Tigran Tonoyan
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Engineering Compressed Static Functions
2018 Data Compression Conference, 2018Recent advances in the compact representation of static functions (with constant access time) have made it possible to fully exploit constructions based on random linear system. Such constructions, albeit theoretically appealing, were previously too slow to be usable.
Marco Genuzio, Sebastiano Vigna
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Performance as a function of compression
IBM Journal of Research and Development, 1998This paper discusses the performance of bilevel-image arithmetic coders, ABIC and JBIG, and Lempel-Ziv string compressors, ALDC and BLDC. Images are analyzed for typical and worst-case throughput and latency as a function of compression. A relationship between the compressibility of an image and the throughput performance of the compression algorithm ...
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On Distributed Compression of Linear Functions
IEEE Transactions on Information Theory, 2008We consider distributed compression of a pair of Gaussian sources in which the goal is to reproduce a linear function of the sources at the decoder. It has recently been noted that lattice codes can provide improved compression rates for this problem compared to conventional, unstructured codes.
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Constructing Compression Functions
2021We have seen that cryptographic hash functions that can process arbitrarily long inputs can be built from fixed-input-length compression functions via the Merkle–Damgard transformation (Chapter 13).
Arno Mittelbach, Marc Fischlin
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On Approximation of Bandlimited Functions with Compressed Sensing
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018The application of Compressed Sensing techniques to bandlimited functions is investigated in this paper. It is shown that under the assumption of sparsity, stable reconstruction of a bandlimited function is possible from finitely many samples, contrary to classical results from signal processing theory.
Huber, Adrian E G, Liu, Shih-Chii
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On the Weak Ideal Compression Functions
2009In SAC 2006, Liskov introduced the weak ideal compression functions. He proved that a hash construction based on these functions is indifferentiable from the random oracle. In ICALP 2008, Hoch and Shamir applied Liskov's idea and proved the indifferentiability of another hash construction.
Akira Numayama, Keisuke Tanaka
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Effects of Tourniquet Compression on Neuromuscular Function
Clinical Orthopaedics and Related Research, 1999Neuromuscular function in New Zealand White rabbits was evaluated after thigh tourniquet compression in the directly compressed quadriceps muscles and the distal tibialis anterior by measuring isometric contractile function after supramaximal stimulation of the motor nerve.
L R, Mohler +3 more
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Sample-distortion functions for compressed sensing
2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2011We consider compressed sensing within a stochastic setting, where the signal or image of interest is drawn from a probability distribution that is in some sense compressible. Within this setting we consider some sample-distortion functions for i.i.d. compressible distributions and derive a simple sample distortion lower bound.
Mike E. Davies 0001, Chunli Guo
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Interactive Function Compression with Asymmetric Priors
2016 Data Compression Conference (DCC), 2016We study the interactive compression of an arbitrary function of two discrete sources with zero-error. The information on the joint distribution of the sources available at the two sides is asymmetric, in that one user knows the true distribution, whereas the other user observes a different distribution. This paper considers the minimum worst-case zero-
Basak Guler +5 more
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