K-nearest Neighbor Search Algorithm Research Articles

Construction of preschool education resource sharing platform based on matrix model

Abstract This paper achieves high-quality sharing of preschool educational resources among learners based on a matrix model. Through the shared resource matrix analysis method, the symmetry of the matrix about this educational resource is judged so that the resource can transmit information implicitly among all subjects. The difference between noise and preschool education resource data points is enhanced using the K-nearest neighbor search algorithm. Combined with the ant colony particle swarm optimization algorithm, the sharing sequence and time of the optimal preschool education resources are calculated, and a preschool education resource-sharing platform is constructed. To verify the applicability of the constructed platform, the simulation analysis results show that the resource search accuracy of the platform constructed in this paper reaches 94.51% on average, and the compatibility performance index is as high as 7.287vw, which is 3.25vw and 3.421vw higher compared to other platforms, respectively. The results show that the matrix model can eliminate resource heterogeneity and provide convenient and efficient interactive technical support for constructing a preschool education resource-sharing platform.

Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks

The communication channel in underwater acoustic sensor networks (UASNs) is time-varying due to the dynamic environmental factors, such as ocean current, wind speed, and temperature profile. Generally, these phenomena occur with a certain regularity, resulting in a similar variation pattern inherited in the communication channels. Based on these observations, the energy efficiency of data transmission can be improved by controlling the modulation method, coding rate, and transmission power according to the channel dynamics. Given the limited computational capacity and energy in underwater nodes, we propose a double-scale adaptive transmission mechanism for the UASNs, where the transmission configuration will be determined by the predicted channel states adaptively. In particular, the historical channel state series will first be decomposed into large-scale and small-scale series and then be predicted by a novel k-nearest neighbor search algorithm with sliding window. Next, an energy-efficient transmission algorithm is designed to solve the problem of long-term modulation and coding optimization. In particular, a quantitative model is constructed to describe the relationship between data transmission and the buffer threshold used in this mechanism, which can then analyze the influence of buffer threshold under different channel states or data arrival rates theoretically. Finally, numerical simulations are conducted to verify the proposed schemes, and results show that they can achieve good performance in terms of channel prediction and energy consumption with moderate buffer length.

Research on Highway Travel Time Estimation Method Based on Feature Matching

In order to solve the problem of inaccurate estimation of expressway travel time, this paper proposes a method of highway travel time estimation based on feature matching. Firstly, historical detector data is used as input to establish historical state library for each road segment, searching states which are similar to the current states. then estimate the travel time of the road segment in the state based on the K-nearest neighbor search algorithm. The advantage of this method is that complicated traffic modeling process is avoided, and the historical state library does not need to be updated in general. The method is based on vissim simulation of various traffic states to verify that the error is within the allowable range, and the results show that the method has strong stability.

A Feasibility Study for a Persistent Homology-Based k-Nearest Neighbor Search Algorithm in Melanoma Detection

Persistent homology is a fairly new branch of computational topology which combines geometry and topology for an effective shape description of use in Pattern Recognition. In particular, it registers through Numbers the presence of holes and their persistence while a parameter (filtering function) is varied. In this paper, some recent developments in this field are integrated in a k-nearest neighbor search algorithm suited for an automatic retrieval of melanocytic lesions. Since long, dermatologists use five morphological parameters (A $$=$$= asymmetry, B $$=$$= boundary, C $$=$$= color, D $$=$$= diameter, E $$=$$= evolution) for assessing the malignancy of a lesion. The algorithm is based on a qualitative assessment of the segmented images by computing both 1 and 2-dimensional persistent Betti Number functions related to the ABCDE parameters and to the internal texture of the lesion. The results of a feasibility test on a set of 107 melanocytic lesions are reported in the section dedicated to the numerical experiments.

Spherical fast multiscale approximation by locally compact orthogonal wavelets

Using a stereographical projection to the plane we construct an \({\mathcal{O}(N\log(N))}\) algorithm to approximate scattered data in N points by orthogonal, compactly supported wavelets on the surface of a 2-sphere or a local subset of it. In fact, the sphere is not treated all at once, but is split into subdomains whose results are combined afterwards. After choosing the center of the area of interest the scattered data points are mapped from the sphere to the tangential plane through that point. By combining a k-nearest neighbor search algorithm and the two dimensional fast wavelet transform a fast approximation of the data is computed and mapped back to the sphere. The algorithm is tested with nearly 1 million data points and yields an approximation with 0.35% relative errors in roughly 2 min on a standard computer using our MATLAB® implementation. The method is very flexible and allows the application of the full range of two dimensional wavelets.

Efficient nearest neighbor query based on extended B +-tree in high-dimensional space

Nearest neighbor queries in high-dimensional space are important in various applications. One-dimensional mapping is an efficient indexing method to speed up the k-nearest neighbor search, which can transform a high-dimensional point into a single-dimensional value indexed by a B +-tree. In this paper, we present a new one-dimensional indexing scheme based on extended B +-tree for k-nearest neighbor search in high-dimensional space. We first partition the high-dimensional dataset and perform Principal Component Analysis on each partition. The distance of each point to the center of the partition is indexed using a B +-tree, and the projection on the first principal component of each point is embedded into leaf node of the B +-tree. In the query, a new filter strategy according to the spatial relationship between the query point and the axis determined by the first principal component is applied to improve the query performance. We also present a novel k-nearest neighbor search algorithm which can guarantee the accuracy of query results. Extensive experiments have been indicative of the effectiveness of our approach.

Fast k-nearest neighbor search algorithm based on pyramid structure of wavelet transform and its application to texture classification

Traditional fast k-nearest neighbor search algorithms based on pyramid structures need either many extra memories or long search time. This paper proposes a fast k-nearest neighbor search algorithm based on the wavelet transform, which exploits the important information hiding in the transform coefficients to reduce the computational complexity. The study indicates that the Haar wavelet transform brings two kinds of important pyramids. Two elimination criteria derived from the transform coefficients are used to reject those impossible candidates. Experimental results on texture classification verify the effectiveness of the proposed algorithm.