Ubiquitous computing for communications and broadcasting

Ubiquitous computing (Ubi-Com) aims to cover the topics of seamless, secure, and intuitive access for distributed processing of various ubiquitous computing applications.Because the technology is evolving into the direction of wireless and the fast processing speed is also getting more attention, there have been many efforts to support the ubiquitous computing through distributed and parallel processing over the scattered networks.Specially, Ubiquitous Communication and Broadcasting are the core technologies for Ubi-Com.This special issue provides an international forum for the presentation and showcase of recent advances on various aspects of Ubiquitous Communication and Broadcasting (UCB).It will reflect the state-of-the-art of the computational methods, involving theory, algorithm, numerical simulation, error and uncertainty analysis and/or novel applications of new processing techniques in engineering, science, and other disciplines related to the UCB.The published papers are expected to focus on novel approaches for the UCB and to present high quality results for tackling problems arising from the ever-growing UCB.This special issue will serve as a landmark source for education, information, and reference to students, professionals, and researchers interested in updating their knowledge about or active in UCB models and services.We have received many manuscripts.Only seven manuscripts of high quality were finally selected for this special issue.Each manuscript selected was blindly reviewed by at least three reviewers consisting of guest editors and external reviewers.We present a brief overview of each manuscript in the following.The first paper entitled 'A sliding window-based false-negative approach for ubiquitous data stream analysis' by Younghee Kim et al. propose a method for a false-negative approach based on the Chernoff bound for efficient analysis of the data stream.Hence, we consider the problem of approximating frequency counts for space-efficient computation over data stream sliding windows.We show that a false-negative approach allowing a controlled number of frequent itemsets to be missing from the output is a more promising solution for mining frequent itemsets from a ubiquitous data stream.These are simple to implement, and have provable quality, space, and time guarantees.The experimental results have shown that the proposed algorithms achieve a high accuracy of at least 99% and require a small execution time.The second paper entitled 'A new query-by-humming system based on the score level fusion of two classifiers' by Gi Pyo Nam et al. propose a new method of query-by-humming (QBH) based on the score level fusion of two classifiers.This research is novel in the following three ways as compared with previous works.First, the features of the humming data are extracted by using musical note estimation based on the spectro-temporal autocorrelation.The extracted features are normalized by using the mean-shifting, median filtering, average filtering, and min-max scaling methods.Second, a pitch-based dynamic time warping method is used as the first classifier.The linear scaling method is used with the quantized binary (QB) code of the pitch data as the second classifier.Third,

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.