We propose an alternate indexing storage technique of Search Engine. In this approach, we achieve reduced space complexity. We try to decrease time complexity for faster data retrieval and decrease storage space for efficient utilization of space. This paper provides an algorithm of indexing mechanism by which effective storage space is reduced. Space complexity of a Search Engine depends on th...

A lot of machine learning algorithms, including clustering methods such as K-nearest neighbor (KNN), highly depend on the distance metrics to understand data pattern well and make right decision based data. In recent years, studies show that can significantly improve performance or deep model in clustering, classification, recovery tasks, etc. this article, we provide a survey widely used chall...

Generative models dealing with modeling a joint data distribution are generally autoencoder, or GAN based. Both have pros and cons, generating blurry images being unstable in training. We propose new generative model resembling classical transforming Gaussian noise into space without adversarial optimization. Training of the proposed is two-step procedure. First, we train an autoencoder-based a...

The signed distance field of a surface can effectively support many geometry processing tasks such as decimation, smoothing, and Boolean operations since it provides efficient access to distance (error) estimates. In this paper we present an algorithm to compute a piecewise linear, not necessarily continuous approximation of the signed distance field for a given object. Our approach is based on...

This paper presents the study on the effect of view distance and movement scale on performance of haptic based teleoperation of a sandblasting robot in complex steel bridge maintenance environments. The operational performance, measured by the Index of Performance (IP), is defined based on the speed and the control accuracy of the manipulator. View distance (i.e. the distance between a display ...

We propose a training and evaluation approach for autoencoder Generative Adversarial Networks (GANs), specifically the Boundary Equilibrium Generative Adversarial Network (BEGAN), based on methods from the image quality assessment literature. Our approach explores a multidimensional evaluation criterion that utilizes three distance functions: an l1 score, the Gradient Magnitude Similarity Mean ...