Real-time ground filtering algorithm of cloud points acquired using Terrestrial Laser Scanner

3D modeling based on point clouds requires ground-filtering algorithms that separate ground from non-ground objects. This study presents two ground filtering algorithms. The first one is based on normal vectors. It has two variants depending on the procedure to compute the k-nearest neighbors. The second algorithm is based on transforming the cloud points into a voxel structure. To evaluate them, the two algorithms are compared according to their execution time, effectiveness and efficiency. Results show that the ground filtering algorithm based on the voxel structure is faster in terms of execution time, effectiveness, and efficiency than the normal vector ground filtering.

Evaluation of bioinspired algorithms for the solution of the job scheduling problem

In this research we used bio-inspired metaheuristics, as artificial immune systems and ant colony algorithms that are based on a number of characteristics and behaviors of living things that are interesting in the computer science area. This paper presents an evaluation of bio-inspired solutions to combinatorial optimization problem, called the Job Shop Scheduling or planning work, in a simple way the objective is to find a configuration or job stream that has the least amount of time to be executed in machine settings. The performance of the algorithms was characterized and evaluated for reference instances of the job shop scheduling problem, comparing the quality of the solutions obtained with respect to the best known solution of the most effective methods. The solutions were evaluated in two aspects, first in relation of quality of solutions, taking as reference the makespan and secondly in relation of performance, taking the number evaluations performed by the algorithm to obtain the best solution.