Laif Swanson

dblp:55/2831 · DBLP profile ↗
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7ranked-venue papers
0as first author
0since 2021 · last 1992
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 3Computer networks · 2Security and privacy · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
6 papers
Coding theory · 68% Information theory · 28% Logic in computer science · 4%
Computer networks
2 papers
Physical-layer communications · 84% Internet architecture and protocols · 16%
Network and information security
1 paper
Cryptographic protocols and secure computation · 100%

Topics — the 21 heaviest of 22, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications › channel modeling › gaussian channel
AWGN channel
0.011992
Performance of binary block codes at low signal-to-noise ratios · IEEE Trans. Inf. Theory 1992
Coding theory › error-correcting codes › block codes
binary block codes
0.011992
Performance of binary block codes at low signal-to-noise ratios · IEEE Trans. Inf. Theory 1992
Coding theory › error-correcting codes
block codes
0.011992
Performance of binary block codes at low signal-to-noise ratios · IEEE Trans. Inf. Theory 1992
Coding theory › multiuser coding
broadcast channel coding
0.011987
A Note on the Wide-Band Gaussian Broadcast Channel · IEEE Trans. Commun. 1987
Information theory
channel capacity
0.011987
A Note on the Wide-Band Gaussian Broadcast Channel · IEEE Trans. Commun. 1987
Information theory › network information theory › broadcast channel
gaussian broadcast channel
0.011987
A Note on the Wide-Band Gaussian Broadcast Channel · IEEE Trans. Commun. 1987
Information theory
time-sharing
0.011987
A Note on the Wide-Band Gaussian Broadcast Channel · IEEE Trans. Commun. 1987
Coding theory › error-correcting codes › error probability analysis
decoding error probability
0.011986
On the decoder error probability for Reed-Solomon codes · IEEE Trans. Inf. Theory 1986
Information theory › information measures › entropy
entropy minimization
0.011986
An entropy maximization problem related to optical communication · IEEE Trans. Inf. Theory 1986
Coding theory
error-correcting codes
0.011986
On the decoder error probability for Reed-Solomon codes · IEEE Trans. Inf. Theory 1986
Coding theory › constrained coding
finite-state encoders
0.011986
An entropy maximization problem related to optical communication · IEEE Trans. Inf. Theory 1986
Coding theory
optical communication
0.011986
An entropy maximization problem related to optical communication · IEEE Trans. Inf. Theory 1986
Coding theory › error-correcting codes
reed-solomon codes
0.011986
On the decoder error probability for Reed-Solomon codes · IEEE Trans. Inf. Theory 1986
Physical-layer communications
channel coding
0.011984
Node Synchronization for the Viterbi Decoder · IEEE Trans. Commun. 1984
Physical-layer communications › channel coding › error control coding
convolutional codes
0.011984
Node Synchronization for the Viterbi Decoder · IEEE Trans. Commun. 1984
Internet architecture and protocols › network synchronization
node synchronization
0.011984
Node Synchronization for the Viterbi Decoder · IEEE Trans. Commun. 1984
Logic in computer science › model theory
infinite structures
0.011982
Infinite Structures in Information Theory · CRYPTO 1982
Cryptographic protocols and secure computation
secret sharing
0.011981
Security Proofs for Information Protection Systems · S&P 1981
Cryptographic protocols and secure computation › secret sharing
threshold secret sharing
0.011981
Security Proofs for Information Protection Systems · S&P 1981
Physical-layer communications › channel coding › convolutional decoding
viterbi decoding
0.011984
Node Synchronization for the Viterbi Decoder · IEEE Trans. Commun. 1984
Quantum computing and quantum information
security proof
0.011981
Security Proofs for Information Protection Systems · S&P 1981

Methods — techniques the papers use, named apart from their topics

error probability analysis · 0.0product measures · 0.0probability bounds · 0.0probabilistic security proofs · 0.0entropy maximization · 0.0statistical detection · 0.0reed-solomon outer coding · 0.0information theory · 0.0
YearPublicationVenuePosition
1992 Performance of binary block codes at low signal-to-noise ratios
abstract
The performance of general binary block codes on an unquantized additive white Gaussian noise (AWGN) channel at low signal-to-noise ratios is considered. Expressions are derived for both the block error and the bit error probabilities near the point where the bit signal-to-noise ratio is zero. These expressions depend on the global geometric structure of the code, although the minimum distance still seems to play a crucial role. Examples of codes such as orthogonal codes, biorthogonal codes, the (24,12) extended Golay code, and the (15,6) expurgated BCH code are discussed. The asymptotic coding gain at low signal-to-noise ratios is also studied.>
Chi-Chao Chao, Robert J. McEliece, Laif Swanson, Eugene R. Rodemich
IEEE Trans. Inf. Theory3
1987 A Note on the Wide-Band Gaussian Broadcast Channel
abstract
Recently, Posner noted that on a wide-band Gaussian broadcast channel, ordinary time-shared coding performs almost as well as more sophisticated broadcast coding strategies. In this note, we shall give a quantitative version of Posner's result and argue that for certain realistic broadcast channels time sharing may suffice.
Robert J. McEliece, Laif Swanson
IEEE Trans. Commun.2
1986 An entropy maximization problem related to optical communication
abstract
Motivated by a problem in optical communication, we consider the general problem of maximizing the entropy of a stationary random process that is subject to an average transition cost constraint. Using a recent result of Justesen and Hoholdt, we present an exact solution to the problem and suggest a class of finite state encoders that give a good approximation to the exact solution.
Robert J. McEliece, Eugene R. Rodemich, Laif Swanson
IEEE Trans. Inf. Theory3
1986 On the decoder error probability for Reed-Solomon codes
abstract
Upper bounds On the decoder error probability for Reed-Solomon codes are derived. By definition, "decoder error" occurs when the decoder finds a codeword other than the transitted codeword; this is in contrast to "decoder failure," which occurs when the decoder fails to find any codeword at all. These results imply, for example, that for aterror-correcting Reed-Solomon code of lengthq - 1over GF(q), if more thanterrors occur, the probability of decoder error is less than1/t!.
Robert J. McEliece, Laif Swanson
IEEE Trans. Inf. Theory2
1984 Node Synchronization for the Viterbi Decoder
abstract
Motivated by the needs of NASA's Voyager 2 mission, in this paper we describe an algorithm which detects and corrects losses of node synchronization in convolutionally encoded data. This algorithm, which would be implemented as a hardware device external to a Viterbi decoder, makes statistical decisions about node synch based on the hard-quantized undecoded data stream. We will show that in a worst-case Voyager environment, our method will detect and correct a true loss of synch (thought to be a very rare event) within several hundred bits; many of the resulting outages will be corrected by the outer Reed-Solomon code. At the same time, the mean time between false alarms is on the order of several years, independent of the signal-to-noise ratio.
Gary Lorden, Robert J. McEliece, Laif Swanson
IEEE Trans. Commun.3
1982 Infinite Structures in Information Theory
G. R. Blakley, Laif Swanson
CRYPTO2
1981 Security Proofs for Information Protection Systems
abstract
Recently discovered procedures use a random input, rather than a cryptographic key, to turn a piece s of information into n + 1 pieces of information in such a way that s can be recovered from any b + 1 of them but that it is hard, or perhaps impossible in a sense which must be precisely defined, to recover s from any b of them. Thus, for example, one might have 15 pieces of information such that any 9 of them suffice to reconstitute s, but euch that no 8 of them give any hint as to what s is. The various authors of euch procedures have called them key safeguarding schemes, threshold schemes, secret sharing, and key sharing. None of these names captures the idea, which we will denote by information protection system (IPS). Our purpose is to put a rigorous foundation under the intuitive security arguments these papers adduce. In the proceed we will produce a distinctive style of proof of security, a rigorous argument involving product measures as a conceptual basis for justifying intuitively plausible probabilistic statement of the sort C. E. Shannon used to describe the security of the one-time pad.
G. R. Blakley, Laif Swanson
S&P2