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We propose PathML, an available bandwidth (i.e., unused capacity of an end-to-end path) estimation method based on a data-driven paradigm that uses machine learning with a large amount of data. An experiment over an operational LTE network was performed to compare our method with prior work.
Accurate human body orientation estimation (HBOE) can significantly promote the analysis of human behavior. However, conventional methods cannot holistically exploit the complementary nature of spatial and temporal information for H-BOE. Different from existing methods, we propose an end-to-end temporal-spatial deep learning framework to accurately estimate the human body orientation. In this framework,...
For nonlinear estimation, the Gaussian sum filter (GSF) provides a flexible and effective framework. It approximates the posterior probability density function (pdf) by a Gaussian mixture in which each Gaussian component is obtained using a linear minimum mean squared error (LMMSE) estimator. However, for a highly nonlinear problem with large measurement noise, the estimation performance of the LMMSE...
In this paper, we study the leader-follower formation control problem of multiple vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) with limited communication. In particular, the leader's trajectory is only accessible to a subset of the followers and the followers only have access to their neighbours' information. Distributed estimators are developed for each VTOL UAV to obtain accurately...
Digitalisation of industrial processes, also called the fourth industrial revolution, is leading to availability of large volume of data containing measurements of many process variables. This offers new opportunities to gain deeper insights on process variability and its effects on quality and performance. Manufacturing facilities already use data driven approaches to study process variability and...
The aim of this work is to estimate the medication adherence of patients with heart failure through the application of a data mining approach on a dataset including information from saliva and breath biomarkers. The method consists of two stages. In the first stage, a model for the estimation of adherence risk of a patient, exploiting anamnestic and instrumental data, is applied. In the second stage,...
Data from vehicles instrumented with GPS or other localization technologies are increasingly becoming widely available due to the investments in Connected and Automated Vehicles (CAVs) and the prevalence of personal mobile devices such as smartphones. Tracking or trajectory data from these probe vehicles are already being used in practice for travel time or speed estimation and for monitoring network...
Our aim is to evaluate fundamental parameters from the analysis of the electromagnetic spectra of stars. We may use 103–105 spectra; each spectrum being a vector with 102–104 coordinates. We thus face the so-called “curse of dimensionality”. We look for a method to reduce the size of this data-space, keeping only the most relevant information. As a reference method, we use principal component analysis...
The process of mining comprises of supervised learning and unsupervised learning. It includes various approaches out of which data classification is one of the beneficial and constructive methods. This paper explores the effective functioning of the whole process. There are several cases in classification where the important data is missed during the process. It can hence be concluded that the process...
Spatiotemporal data mining finds great importance with the increasing availability of spatiotemporal datasets in tremendous amounts. This work introduces an algorithm that identifies the clusters with respect to the spatial and temporal properties of objects. Ordinary density based algorithms like DBSCAN consider only spatial properties and it takes the spatial distance parameter and number of minimum...
Traditional data stream classification techniques assume that the stream of data is generated from a single non-stationary process. On the contrary, a recently introduced problem setting, referred to as Multistream Classification involves two independent non-stationary data generating processes. One of them is the source stream that continuously generates labeled data instances. The other one is the...
Key Frame Extraction (KFE) is an important block involved with any search process on large scale video logs. KFE has wide applications in fields like Content based retrieval systems, Video Summarization, compression and Video content management. The conventional algorithms exploit the pixel similarities or histogram distributions between frames of video, ignoring the key frame metric information in...
As part of an ongoing research into extracting mission-critical information from Search and Rescue speech communications, a corpus of unscripted, goal-oriented, two-party spoken conversations has been designed and collected. The Sheffield Search and Rescue (SSAR) corpus comprises about 12 hours of data from 96 conversations by 24 native speakers of British English with a southern accent. Each conversation...
Data mining plays an efficient role in prediction of diseases in health care industry. Diabetes is one of the major global health problems. According to WHO 2014 report, around 422 million people worldwide are suffering from diabetes. Diabetes is a metabolic disease where the improper management of blood glucose levels led to risk of generating abnormalities in functioning of critical organs like...
Estimation of people density in intensely dense crowded scenes is very crucial due to perspective difference, few pixels per target, clutter and complex backgrounds etc. Most of the existing work is unable to handle the crowds of hundreds or thousands. At this level of density, one feature is not enough to estimate the total density of an image. We propose a hybrid model which relies on multiple source...
Indoor systems cannot obtain a precise estimate of the location, due to unstable signals. In this paper, we use realistic wireless data from the IEEE International Conference on Data Mining (ICDM) dataset and Azure Machine Learning Studio to perform Bagging (also called bootstrap aggregating). By using the machine leaning technique in the Azure Machine Learning Studio, we can obtain more than 69 percent...
Originally developed for purely functional verification of software, native or host compiled simulation [6] has gained momentum, thanks to its considerable speedup compared to instruction set simulation (ISS). To obtain a performance model of the software, non-functional information is computed from the target binary code using low-level analysis and back-annotated into the highlevel code used to...
Parallelizing compilers are a promising solution to tackle key challenges ofMPSoC programming. One fundamental aspect for a profitable parallelization is to estimate the performance of the applications on the target platforms. There is a wide range of state-of-theart performance estimation techniques, such as, simulation-based, measurement-based, among others. They provide performance estimates typically...
Native simulation is an interesting virtual prototyping candidate to speed-up architecture exploration and early software developments. It however does not provide out-of-the box non-functional information needed for software performance estimation. Annotating software with information is complex as highlevel codes and binary codes have different structures due to compiler optimizations. This work...
The paper presents a comprehensive approach using clustering based data mining for load curves characterization in real distribution networks. The load curves characterized by their main indicators is made using information provided by Smart Meters. The proposed method was tested using a real database with 60 rural substations. The results demonstrate the ability of the methodology to be efficiently...
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