Efficient String Matching on Coded Texts

The so called "four Russians technique'' is often used to speed up algorithms by encoding several data items in a single memory cell. Given a sequence of n symbols over a constant size alphabet, one can encode the sequence into O(n / lambda) memory cells in O(log(lambda) ) time using n / log(lambda) processors. This paper presents an efficient CRCW-PRAM string-matching algorithm for coded texts that takes O(log log(m/lambda)) time making only O(n / lambda ) operations, an improvement by a factor of lambda = O(log n) on the number of operations used in previous algorithms. Using this string-matching algorithm one can test if a string is square-free and find all palindromes in a string in O(log log n) time using n / log log n processors

Optimally Fast Parallel Algorithms for Preprocessing and Pattern Matching in One and Two Dimensions

All algorithms below are optimal alphabet-independent parallel CRCW PRAM algorithms. In one dimension: Given a pattern string of length m for the string-matching problem, we design an algorithm that computes a deterministic sample of a sufficiently long substring in constant time. This problem used to be a bottleneck in the pattern preprocessing for one- and two-dimensional pattern matching. The best previous time bound was O(log 2 m= log log m). We use this algorithm to obtain the following results. 1. Improving the preprocessing of the constant-time text search algorithm [12] from O(log 2 m= log log m) to O(log log m), which is now best possible. 2. A constant-time deterministic string-matching algorithm in the case that the text length n satisfies n = \Omega\Gamma m 1+ffl ) for a constant ffl ? 0. 3. A simple probabilistic string-matching algorithm that has constant time with high probability for random input. 4. A constant expected time Las-Vegas algorithm for computing t..

Swarm Based Implementation of a Virtual Distributed Database System in a Sensor Network

The deployment of unmanned aerial vehicles (UAVs) in recent military operations has had success in carrying out surveillance and combat missions in sensitive areas. An area of intense research on UAVs has been on controlling a group of small-sized UAVs to carry out reconnaissance missions normally undertaken by large UAVs such as Predator or Global Hawk. A control strategy for coordinating the UAV movements of such a group of UAVs adopts the bio-inspired swarm model to produce autonomous group behavior. This research proposes establishing a distributed database system on a group of swarming UAVs, providing for data storage during a reconnaissance mission. A distributed database system model is simulated treating each UAV as a distributed database site connected by a wireless network. In this model, each UAV carries a sensor and communicates to a command center when queried. Drawing equivalence to a sensor network, the network of UAVs poses as a dynamic ad-hoc sensor network. The distributed database system based on a swarm of UAVs is tested against a set of reconnaissance test suites with respect to evaluating system performance. The design of experiments focuses on the effects of varying the query input and types of swarming UAVs on overall system performance. The results show that the topology of the UAVs has a distinct impact on the output of the sensor database. The experiments measuring system delays also confirm the expectation that in a distributed system, inter-node communication costs outweigh processing costs