3,776 research outputs found
A Home Security System Based on Smartphone Sensors
Several new smartphones are released every year. Many people upgrade to new phones, and their old phones are not put to any further use. In this paper, we explore the feasibility of using such retired smartphones and their on-board sensors to build a home security system. We observe that door-related events such as opening and closing have unique vibration signatures when compared to many types of environmental vibrational noise. These events can be captured by the accelerometer of a smartphone when the phone is mounted on a wall near a door. The rotation of a door can also be captured by the magnetometer of a smartphone when the phone is mounted on a door. We design machine learning and threshold-based methods to detect door opening events based on accelerometer and magnetometer data and build a prototype home security system that can detect door openings and notify the homeowner via email, SMS and phone calls upon break-in detection. To further augment our security system, we explore using the smartphone’s built-in microphone to detect door and window openings across multiple doors and windows simultaneously. Experiments in a residential home show that the accelerometer- based detection can detect door open events with an accuracy higher than 98%, and magnetometer-based detection has 100% accuracy. By using the magnetometer method to automate the training phase of a neural network, we find that sound-based detection of door openings has an accuracy of 90% across multiple doors
Review on smartphone sensing technology for structural health monitoring
Sensing is a critical and inevitable sector of structural health monitoring (SHM). Recently, smartphone sensing technology has become an emerging, affordable, and effective system for SHM and other engineering fields. This is because a modern smartphone is equipped with various built-in sensors and technologies, especially a triaxial accelerometer, gyroscope, global positioning system, high-resolution cameras, and wireless data communications under the internet-of-things paradigm, which are suitable for vibration- and vision-based SHM applications. This article presents a state-of-the-art review on recent research progress of smartphone-based SHM. Although there are some short reviews on this topic, the major contribution of this article is to exclusively present a compre- hensive survey of recent practices of smartphone sensors to health monitoring of civil structures from the per- spectives of measurement techniques, third-party apps developed in Android and iOS, and various application domains. Findings of this article provide thorough understanding of the main ideas and recent SHM studies on smartphone sensing technology
AccelPrint:Accelerometers are Different by Birth
This paper submits a hypothesis that smartphone accelerometers possess unique fingerprints. We believe that the fingerprints arise from hardware imperfections during the sensor manufacturing process, causing every sensor chip to respond differently to the same motion stimulus. The differences in responses are subtle enough that they do not affect most of the higher level functions computed on them. Nonetheless, upon close inspection, these fingerprints emerge with consistency, and can even be somewhat independent of the stimulus that generates them. Measurements and classification on 80 standalone accelerometer chips, 25 Android phones, and 2 tablets, show precision and recall upward of 96%, along with good robustness to real-world conditions. Unsurprisingly, such sensor fingerprints invite new threats in smartphone applications. A crowd-sourcing app running in the cloud could segregate sensor data for each device, making it easy to track a user over space and time. This paper makes the case that such attacks are almost trivial to launch, while simple solutions may not be adequate to counteract them
Tüübituletus neljandat järku loogikavalemitele
Tänapäeval omavad nutiseadmed meie elus suurt rolli, eriti igapäevastes tegemistes.
Sellepärast võib kaaluda nutitelefoni kui üht kõige huvitavamat andurit kujutamaks meie
tegevusi ja meie ümbrust. Lisaks sellele on nutitelefonide arvutusjõudlus hüppeliselt
kasvanud, mida kinnitavad nendes sisalduvad erinevad andurid nagu kiirendusmõõturid
ja güroskoobid ning võimekus sooritada rohkem ülesandeid kui kunagi varem. Nende
mugavuse ja madala hinna tõttu on nutitelefone hakatud kasutama kui kaasaskantavaid
arvutusplatvorme autonoomsete sõidukite arenduses.
Intelligentsete sõidukite süsteemide kriitiliseimaks probleemiks on turvalisus. Teekatte
tuvastus on üks turvalise liiklemise põhikomponentidest. Enamik praeguseid lahendusi
teekatte tuvastamiseks kasutavad erinevate sensorite nagu kaamerate ja LiDARite kokkusulatamist.
See on küll efektiivne meetod, kuid tegemist on kallite anduritega ning mille
kasutamine vajab auto enda modifitseerimist.
Lõputöö pakub välja meetodi teekatte tuvastamiseks kasutades nutitelefonis oleva
kiirendusmõõturi andmeid. See protsess kasutab ajaliselt jätjestatud kiirendusmõõturi
andmeid, millele järgneb masiivne ajaliselt järjestatud tunnuste eraldamine ja valimine.
Peale seda suunatakse eraldatud tunnused DeepSense närvivõrgu raamistikku, et teekate
tuvastada. Meetod klassifitseerib kolme erinevat teekatte tüüpi: sile, munakivitee ja
kruusatee.
Põhjalik pakutud metoodika uurimine ja analüüs viiakse läbi kasutades üldlevinud
masinõppe meetodeid nagu tugivektor-masinad, otsustusmets, täielikult ühendatud närvivõrgud
ja konvulutioonteisendus närvivõrgud. Metoodikal põhinevad katsed näitavad, et
pakutud lähenemine võimaldab tuvastada teekatte siledust väljapakutud kolme kategooriasse.Nowadays, Smart devices plays a big role in our lives, especially in our daily activities.
Therefore, Smartphones can be considered as one of the most interesting sensor for
depicting our activities and our surroundings. Furthermore, the computation power of
smartphones has increased a lot recently as most of them have multiple sensors like
accelerometers and gyroscopes. Besides, They are capable of processing more tasks than
we ever imagined. Because of their advantages of convenience and low-cost, the portable
computation platforms has been adopted in the development of autonomous vehicles.
The most critical issue of the intelligent system assisted vehicles is that the safety
problem. The recognition of the road surface is one of the components to ensure the
safety drive. Most of the solutions use sensor fusion to recognize road surfaces such as
combining cameras and LiDARs, which is costly for equipment and they usually need
installations to re-equip existing cars, but these methods provide overall excellent results.
This thesis proposes a method for recognizing the road surface based on using accelerometer
data collected from smartphone. The process uses time series data collected
from a smartphone’s accelerometer, followed by a massive time series feature extraction
and selection. After that, the features are fed into trained DeepSense variant neural
network framework to get the recognition of the road surfaces. The proposed method
provides three classes recognition for smooth, bumpy and rough roads.
Moreover, in this thesis we conducted a thorough evaluation and analysis of the
proposed method by comparing it with conventional machine learning methods like
SVM, random forest, fully connected neural network and convolutional neural network.
The accuracy of the method in this thesis overmatch the compared examples. The road
surface type will be classified into three categories which will indicate smoothness of the
road surface
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