755 research outputs found
Fall Detection System with Accelerometer and Threshold-based Algorithm
Most presently available fall detection systems that are marketed for commercial use predominantly consist of wearable technologies. These technologies often involve a device positioned on the wrist, which may lead to the occurrence of false positive alerts due to the movements of the wrist. This paper proposed a fall detection system that aims to improve both reliability and cost-effectiveness. The system is designed to promptly inform surrounding individuals of their need for assistance in emergency situations. The fall detection system we propose consists of an accelerometer and a gyroscope, which collectively calculate acceleration, orientation, and various other motion characteristics. The resulting system demonstrated a sensitivity of 90%, a specificity of 85%, and an accuracy of 87.5%
Online Active Linear Regression via Thresholding
We consider the problem of online active learning to collect data for
regression modeling. Specifically, we consider a decision maker with a limited
experimentation budget who must efficiently learn an underlying linear
population model. Our main contribution is a novel threshold-based algorithm
for selection of most informative observations; we characterize its performance
and fundamental lower bounds. We extend the algorithm and its guarantees to
sparse linear regression in high-dimensional settings. Simulations suggest the
algorithm is remarkably robust: it provides significant benefits over passive
random sampling in real-world datasets that exhibit high nonlinearity and high
dimensionality --- significantly reducing both the mean and variance of the
squared error.Comment: Published in AAAI 201
Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea
Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy ~99.16%, probability of detection (POD) ~93.08%, probability of false detection (POFD) ~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD ~ 93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD ~ 50-60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires
On the Power of Threshold-Based Algorithms for Detecting Cycles in the CONGEST Model
It is known that, for every , -freeness can be decided by a
generic Monte-Carlo algorithm running in rounds in the
CONGEST model. For , faster Monte-Carlo algorithms do exist,
running in rounds, based on upper bounding the number of
messages to be forwarded, and aborting search sub-routines for which this
number exceeds certain thresholds. We investigate the possible extension of
these threshold-based algorithms, for the detection of larger cycles. We first
show that, for every , there exists an infinite family of graphs
containing a -cycle for which any threshold-based algorithm fails to detect
that cycle. Hence, in particular, neither -freeness nor
-freeness can be decided by threshold-based algorithms. Nevertheless,
we show that -freeness can still be decided by a
threshold-based algorithm, running in rounds,
which is faster than using the generic algorithm, which would run in
rounds. Moreover, we exhibit an
infinite collection of families of cycles such that threshold-based algorithms
can decide -freeness for every in this collection.Comment: to be published in SIROCCO 202
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