210,446 research outputs found
Adaptive Non-Linear Pattern Matching Automata
Efficient pattern matching is fundamental for practical term rewrite engines. By preprocessing the given patterns into a finite deterministic automaton the matching patterns can be decided in a single traversal of the relevant parts of the input term. Most automaton-based techniques are restricted to linear patterns, where each variable occurs at most once, and require an additional post-processing step to check so-called variable consistency. However, we can show that interleaving the variable consistency and pattern matching phases can reduce the number of required steps to find a match all matches. Therefore, we take the existing adaptive pattern matching automata as introduced by Sekar et al and extend it these with consistency checks. We prove that the resulting deterministic pattern matching automaton is correct, and show that its evaluation depth is can be shorter than two-phase approaches
Adaptive Multi-Pattern Fast Block-Matching Algorithm Based on Motion Classification Techniques
Motion estimation is the most time-consuming subsystem in a video codec. Thus, more efficient methods of motion estimation should be investigated. Real video sequences usually exhibit a wide-range of motion content as well as different degrees of detail, which become particularly difficult to manage by typical block-matching algorithms. Recent developments in the area of motion estimation have focused on the adaptation to video contents. Adaptive thresholds and multi-pattern search algorithms have shown to achieve good performance when they success to adjust to motion characteristics. This paper proposes an adaptive algorithm, called MCS, that makes use of an especially tailored classifier that detects some motion cues and chooses the search pattern that best fits to them. Specifically, a hierarchical structure of binary linear classifiers is proposed. Our experimental results show that MCS notably reduces the computational cost with respect to an state-of-the-art method while maintaining the qualityPublicad
Vision-based adaptive cruise control using pattern matching
Adaptive Cruise Control (ACC) is a relatively new system designed to assist automobile drivers in maintaining a safe following distance. This paper proposes and validates a vision-based ACC system which uses a single camera to obtain the clearance distance between the preceding vehicle and the ACC vehicle. Pattern matching, with the aid of lane detection, is used for vehicle detection. The vehicle and range detection algorithms are validated using real-world data, and then the resulting system performance is shown to be sufficient using a simulation of a basic vehicle model
Centroid Detection by Gaussian Pattern Matching in Adaptive Optics
Shack Hartmann wavefront sensor is a two dimensional array of lenslets which
is used to detect the incoming phase distorted wavefront through local tilt
measurements made by recording the spot pattern near the focal plane. Wavefront
reconstruction is performed in two stages - (a) image centroiding to calculate
local slopes, (b) formation of the wavefront shape from local slope
measurement. Centroiding accuracy contributes to most of the wavefront
reconstruction error in Shack Hartmann sensor based adaptive optics system with
readout and background noise. It becomes even more difficult in atmospheric
adaptive optics case, where scintillation effects may also occur. In this paper
we used a denoising technique based on thresholded Zernike reconstructor to
minimize the effects due to readout and background noise. At low signal to
noise ratio, this denoising technique can be improved further by taking the
advantage of the shape of the spot. Assuming a Gaussian pattern for individual
spots, it is shown that the centroiding accuracy can be improved in the
presence of strong scintillations and background.Comment: 6 pages, 14 figures, Accepted for publication in the International
Journal of Futuristic Computer Application
An adaptive hybrid pattern-matching algorithm on indeterminate strings
We describe a hybrid pattern-matching algorithm that works on both regular and indeterminate strings. This algorithm is inspired by the recently proposed hybrid algorithm FJS and its indeterminate successor. However, as discussed in this paper, because of the special properties of indeterminate strings, it is not straightforward to directly migrate FJS to an indeterminate version. Our new algorithm combines two fast pattern-matching algorithms, ShiftAnd and BMS (the Sunday variant of the Boyer-Moore algorithm), and is highly adaptive to the nature of the text being processed. It avoids using the border array, therefore avoids some of the cases that are awkward for indeterminate strings. Although not always the fastest in individual test cases, our new algorithm is superior in overall performance to its two component algorithms â perhaps a general advantage of hybrid algorithms
Chaotic exploration and learning of locomotion behaviours
We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage
Adaptive Kernel Matching Pursuit for Pattern Classification
A sparse classifier is guaranteed to generalize better than a denser one, given they perform identical on the training set. However, methods like Support Vector Machine, even if they produce relatively sparse models, are known to scale linearly as the number of training examples increases. A recent proposed method, the Kernel Matching Pursuit, presents a number of advantages over th
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