Stefan Nilsson

Bio: Stefan Nilsson is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Laser & Semiconductor laser theory. The author has an hindex of 24, co-authored 71 publications receiving 2450 citations. Previous affiliations of Stefan Nilsson include Helsinki University of Technology & Lund University.

IP-address lookup using LC-tries

Abstract: There has been a notable interest in the organization of routing information to enable fast lookup of IP addresses. The interest is primarily motivated by the goal of building multigigabit routers for the Internet, without having to rely on multilayer switching techniques. We address this problem by using an LC-trie, a trie structure with combined path and level compression. This data structure enables us to build efficient, compact, and easily searchable implementations of an IP-routing table. The structure can store both unicast and multicast addresses with the same average search times. The search depth increases as /spl Theta/(log log n) with the number of entries in the table for a large class of distributions, and it is independent of the length of the addresses. A node in the trie can be coded with four bytes. Only the size of the base vector, which contains the search strings, grows linearly with the length of the addresses when extended from 4 to 16 bytes, as mandated by the shift from IP version 4 to IP version 6. We present the basic structure as well as an adaptive version that roughly doubles the number of lookups/s. More general classifications of packets that are needed for link sharing, quality-of-service provisioning, and multicast and multipath routing are also discussed. Our experimental results compare favorably with those reported previously in the research literature.

Fast address look-up for internet routers

Abstract: We consider the problem of organizing address tables for internet routers to enable fast searching. Our proposal is to to build an efficient, compact and easily searchable implementation of an IP routing table by using an LC-trie, a trie structure with combined path and level compression. The depth of this structure increases very slowly as function of the number of entries in the table. A node can be coded in only four bytes and the size of the main search structure never exceeds 256 kB for the tables in the US core routers. We present a software implementation that can sustain approximately half a million lookups per second on a 133 MHz Pentium personal computer, and two million lookups per second on a more powerful SUN Sparc Ultra II workstation.

Sorting in linear time

Abstract: We show that a unit-cost RAM with a word length of bits can sort integers in the range in time, for arbitrary ! , a significant improvement over the bound of " # $ achieved by the fusion trees of Fredman and Willard. Provided that % & ' ( *),+., for some fixed /102 , the sorting can even be accomplished in linear expected time with a randomized algorithm. Both of our algorithms parallelize without loss on a unitcost PRAM with a word length of bits. The first one yields an algorithm that uses 3 4 5 $ time and 6 ( operations on a deterministic CRCW PRAM. The second one yields an algorithm that uses ' 5 7 expected time and " expected operations on a randomized EREW PRAM, provided that 8 ' 5 7 *),+.for some fixed /90: . Our deterministic and randomized sequential and parallel algorithms generalize to the lexicographic sorting problem of sorting multiple-precision integers represented in several words.

30 GHz direct modulation bandwidth in detuned loaded InGaAsP DBR lasers at 1.55 /spl mu/m wavelength

Abstract: An increased resonance frequency and reduced damping of theresonance peak leading to a record high modulation bandwidth of30GHz were observed in 1.55 mm InGaAsP DBR lasers. Theseresults are attributed to the mechanism of detuned loading.

74 nm wavelength tuning range of an InGaAsP/InP vertical grating assisted codirectional coupler laser with rear sampled grating reflector

Abstract: A vertical-grating-assisted codirectional coupler laser with rear sampled grating reflector (GCSR laser) combines single-mode operation over a very wide tuning range of 74 nm with a side-mode suppression of better than 30 dB over most of the wavelength span. The total tuning span is limited by the combined bandwidths of the active material gain curve and the envelope of the sampled grating reflection peak spectrum, while the tuning capacity of the directional coupler filter alone is estimated to be at least 140 nm. >

Introduction to Algorithms, Second Edition

Abstract: problems To understand the class of polynomial-time solvable problems, we must first have a formal notion of what a "problem" is. We define an abstract problem Q to be a binary relation on a set I of problem instances and a set S of problem solutions. For example, an instance for SHORTEST-PATH is a triple consisting of a graph and two vertices. A solution is a sequence of vertices in the graph, with perhaps the empty sequence denoting that no path exists. The problem SHORTEST-PATH itself is the relation that associates each instance of a graph and two vertices with a shortest path in the graph that connects the two vertices. Since shortest paths are not necessarily unique, a given problem instance may have more than one solution. This formulation of an abstract problem is more general than is required for our purposes. As we saw above, the theory of NP-completeness restricts attention to decision problems: those having a yes/no solution. In this case, we can view an abstract decision problem as a function that maps the instance set I to the solution set {0, 1}. For example, a decision problem related to SHORTEST-PATH is the problem PATH that we saw earlier. If i = G, u, v, k is an instance of the decision problem PATH, then PATH(i) = 1 (yes) if a shortest path from u to v has at most k edges, and PATH(i) = 0 (no) otherwise. Many abstract problems are not decision problems, but rather optimization problems, in which some value must be minimized or maximized. As we saw above, however, it is usually a simple matter to recast an optimization problem as a decision problem that is no harder. Encodings If a computer program is to solve an abstract problem, problem instances must be represented in a way that the program understands. An encoding of a set S of abstract objects is a mapping e from S to the set of binary strings. For example, we are all familiar with encoding the natural numbers N = {0, 1, 2, 3, 4,...} as the strings {0, 1, 10, 11, 100,...}. Using this encoding, e(17) = 10001. Anyone who has looked at computer representations of keyboard characters is familiar with either the ASCII or EBCDIC codes. In the ASCII code, the encoding of A is 1000001. Even a compound object can be encoded as a binary string by combining the representations of its constituent parts. Polygons, graphs, functions, ordered pairs, programs-all can be encoded as binary strings. Thus, a computer algorithm that "solves" some abstract decision problem actually takes an encoding of a problem instance as input. We call a problem whose instance set is the set of binary strings a concrete problem. We say that an algorithm solves a concrete problem in time O(T (n)) if, when it is provided a problem instance i of length n = |i|, the algorithm can produce the solution in O(T (n)) time. A concrete problem is polynomial-time solvable, therefore, if there exists an algorithm to solve it in time O(n) for some constant k. We can now formally define the complexity class P as the set of concrete decision problems that are polynomial-time solvable. We can use encodings to map abstract problems to concrete problems. Given an abstract decision problem Q mapping an instance set I to {0, 1}, an encoding e : I → {0, 1}* can be used to induce a related concrete decision problem, which we denote by e(Q). If the solution to an abstract-problem instance i I is Q(i) {0, 1}, then the solution to the concreteproblem instance e(i) {0, 1}* is also Q(i). As a technicality, there may be some binary strings that represent no meaningful abstract-problem instance. For convenience, we shall assume that any such string is mapped arbitrarily to 0. Thus, the concrete problem produces the same solutions as the abstract problem on binary-string instances that represent the encodings of abstract-problem instances. We would like to extend the definition of polynomial-time solvability from concrete problems to abstract problems by using encodings as the bridge, but we would like the definition to be independent of any particular encoding. That is, the efficiency of solving a problem should not depend on how the problem is encoded. Unfortunately, it depends quite heavily on the encoding. For example, suppose that an integer k is to be provided as the sole input to an algorithm, and suppose that the running time of the algorithm is Θ(k). If the integer k is provided in unary-a string of k 1's-then the running time of the algorithm is O(n) on length-n inputs, which is polynomial time. If we use the more natural binary representation of the integer k, however, then the input length is n = ⌊lg k⌋ + 1. In this case, the running time of the algorithm is Θ (k) = Θ(2), which is exponential in the size of the input. Thus, depending on the encoding, the algorithm runs in either polynomial or superpolynomial time. The encoding of an abstract problem is therefore quite important to our under-standing of polynomial time. We cannot really talk about solving an abstract problem without first specifying an encoding. Nevertheless, in practice, if we rule out "expensive" encodings such as unary ones, the actual encoding of a problem makes little difference to whether the problem can be solved in polynomial time. For example, representing integers in base 3 instead of binary has no effect on whether a problem is solvable in polynomial time, since an integer represented in base 3 can be converted to an integer represented in base 2 in polynomial time. We say that a function f : {0, 1}* → {0,1}* is polynomial-time computable if there exists a polynomial-time algorithm A that, given any input x {0, 1}*, produces as output f (x). For some set I of problem instances, we say that two encodings e1 and e2 are polynomially related if there exist two polynomial-time computable functions f12 and f21 such that for any i I , we have f12(e1(i)) = e2(i) and f21(e2(i)) = e1(i). That is, the encoding e2(i) can be computed from the encoding e1(i) by a polynomial-time algorithm, and vice versa. If two encodings e1 and e2 of an abstract problem are polynomially related, whether the problem is polynomial-time solvable or not is independent of which encoding we use, as the following lemma shows. Lemma 34.1 Let Q be an abstract decision problem on an instance set I , and let e1 and e2 be polynomially related encodings on I . Then, e1(Q) P if and only if e2(Q) P. Proof We need only prove the forward direction, since the backward direction is symmetric. Suppose, therefore, that e1(Q) can be solved in time O(nk) for some constant k. Further, suppose that for any problem instance i, the encoding e1(i) can be computed from the encoding e2(i) in time O(n) for some constant c, where n = |e2(i)|. To solve problem e2(Q), on input e2(i), we first compute e1(i) and then run the algorithm for e1(Q) on e1(i). How long does this take? The conversion of encodings takes time O(n), and therefore |e1(i)| = O(n), since the output of a serial computer cannot be longer than its running time. Solving the problem on e1(i) takes time O(|e1(i)|) = O(n), which is polynomial since both c and k are constants. Thus, whether an abstract problem has its instances encoded in binary or base 3 does not affect its "complexity," that is, whether it is polynomial-time solvable or not, but if instances are encoded in unary, its complexity may change. In order to be able to converse in an encoding-independent fashion, we shall generally assume that problem instances are encoded in any reasonable, concise fashion, unless we specifically say otherwise. To be precise, we shall assume that the encoding of an integer is polynomially related to its binary representation, and that the encoding of a finite set is polynomially related to its encoding as a list of its elements, enclosed in braces and separated by commas. (ASCII is one such encoding scheme.) With such a "standard" encoding in hand, we can derive reasonable encodings of other mathematical objects, such as tuples, graphs, and formulas. To denote the standard encoding of an object, we shall enclose the object in angle braces. Thus, G denotes the standard encoding of a graph G. As long as we implicitly use an encoding that is polynomially related to this standard encoding, we can talk directly about abstract problems without reference to any particular encoding, knowing that the choice of encoding has no effect on whether the abstract problem is polynomial-time solvable. Henceforth, we shall generally assume that all problem instances are binary strings encoded using the standard encoding, unless we explicitly specify the contrary. We shall also typically neglect the distinction between abstract and concrete problems. The reader should watch out for problems that arise in practice, however, in which a standard encoding is not obvious and the encoding does make a difference. A formal-language framework One of the convenient aspects of focusing on decision problems is that they make it easy to use the machinery of formal-language theory. It is worthwhile at this point to review some definitions from that theory. An alphabet Σ is a finite set of symbols. A language L over Σ is any set of strings made up of symbols from Σ. For example, if Σ = {0, 1}, the set L = {10, 11, 101, 111, 1011, 1101, 10001,...} is the language of binary representations of prime numbers. We denote the empty string by ε, and the empty language by Ø. The language of all strings over Σ is denoted Σ*. For example, if Σ = {0, 1}, then Σ* = {ε, 0, 1, 00, 01, 10, 11, 000,...} is the set of all binary strings. Every language L over Σ is a subset of Σ*. There are a variety of operations on languages. Set-theoretic operations, such as union and intersection, follow directly from the set-theoretic definitions. We define the complement of L by . The concatenation of two languages L1 and L2 is the language L = {x1x2 : x1 L1 and x2 L2}. The closure or Kleene star of a language L is the language L*= {ε} L L L ···, where Lk is the language obtained by

Microwave photonics

Abstract: An overview of analog microwave photonics will be presented. The performance requirements for externally-modulated analog microwave photonic links will be reviewed with specific emphasis placed on modulator efficiency, laser noise, detected photocurrent, and link linearity.

Electrically pumped hybrid AlGaInAs-silicon evanescent laser

Abstract: An electrically pumped light source on silicon is a key element needed for photonic integrated circuits on silicon. Here we report an electrically pumped AlGaInAs-silicon evanescent laser architecture where the laser cavity is defined solely by the silicon waveguide and needs no critical alignment to the III-V active material during fabrication via wafer bonding. This laser runs continuous-wave (c.w.) with a threshold of 65 mA, a maximum output power of 1.8 mW with a differential quantum efficiency of 12.7 % and a maximum operating temperature of 40 degrees C. This approach allows for 100's of lasers to be fabricated in one bonding step, making it suitable for high volume, low-cost, integration. By varying the silicon waveguide dimensions and the composition of the III-V layer, this architecture can be extended to fabricate other active devices on silicon such as optical amplifiers, modulators and photo-detectors.

Compressed full-text indexes

Abstract: Full-text indexes provide fast substring search over large text collections. A serious problem of these indexes has traditionally been their space consumption. A recent trend is to develop indexes that exploit the compressibility of the text, so that their size is a function of the compressed text length. This concept has evolved into self-indexes, which in addition contain enough information to reproduce any text portion, so they replace the text. The exciting possibility of an index that takes space close to that of the compressed text, replaces it, and in addition provides fast search over it, has triggered a wealth of activity and produced surprising results in a very short time, which radically changed the status of this area in less than 5 years. The most successful indexes nowadays are able to obtain almost optimal space and search time simultaneously.In this article we present the main concepts underlying (compressed) self-indexes. We explain the relationship between text entropy and regularities that show up in index structures and permit compressing them. Then we cover the most relevant self-indexes, focusing on how they exploit text compressibility to achieve compact structures that can efficiently solve various search problems. Our aim is to give the background to understand and follow the developments in this area.

[서평]「Computer Organization and Design, The Hardware/Software Interface」

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