The mobile device for identification of control objects: evaluation of noise algorithm identification

2015 ◽  
Author(s):  
V.I. Goncharov ◽  
I.O. Ilyin ◽  
A.V. Kudryavtsev ◽  
B.I. Pyakillya
Keyword(s):  
Mobile Device

Serum and MRI Biomarkers in Mobile Device Texting: A Pilot Study

2012 ◽  
Author(s):  
Judith E. Gold ◽  
Feroze B. Mohamed ◽  
Sayed Ali ◽  
Mary F. Barbe
Keyword(s):  
Pilot Study ◽  
Mobile Device

Author response for "Use of passive sensing to quantify adolescent mobile device usage: Feasibility, acceptability, and preliminary validation of the eMoodie application"

2020 ◽  
Author(s):  
Sarah E. Domoff ◽  
Claire Ann Banga ◽  
Aubrey L. Borgen ◽  
Ryan P. Foley ◽  
Chelsea Robinson ◽  
...  
Keyword(s):  
Mobile Device ◽  
Author Response ◽  
Passive Sensing ◽  
Preliminary Validation

Analisis Forensik Smartphone Android Menggunakan Metode NIST dan Tool MOBILedit Forensic Express

2020 ◽  
Vol 5 (1) ◽  
pp. 89
Author(s):  
Nasirudin Nasirudin ◽  
Sunardi Sunardi ◽  
Imam Riadi
Keyword(s):  
Mobile Device ◽  
Rapid Development ◽  
Rapid Progress ◽  
Data Backup ◽  
Technological Advances ◽  
Smartphone Technology ◽  
Case Simulation ◽  
Device Technology

Technological advances are growing rapidly, including mobile device technology, one of which is an Android smartphone that is experiencing rapid progress with a variety of features so that it can spoil its users, with the rapid development of smartphone technology, many users benefit, but many are disadvantaged by the growing smartphone. technology, so that many perpetrators or persons who commit crimes and seek profits with smartphone facilities. Case simulation by securing Samsung Galaxy A8 brand android smartphone evidence using the MOBILedit forensic express forensic tool with the National Institute of Standards and Technology (NIST) method which consists of four stages of collection, examination, analysis and reporting. The results of testing the Samsung Galaxy A8 android smartphone are carried out with the NIST method and the MOBILedit Forensic Express tool obtained by data backup, extraction and analysis so that there are findings sought for investigation and evidence of crimes committed by persons using android smartphone facilities.

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Computational Cost ◽  
False Positives ◽  
Compact Model ◽  
High Quality ◽  
False Negatives ◽  
Trade Off ◽  
Depth Reconstruction ◽  
Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

A MOBILE DEVICE FOR MEASURING SENSORIMOTOR TIMING IN SYNCHRONIZED TAPPING

2004 ◽  
Vol 98 (3) ◽  
pp. 1327 ◽  
Author(s):  
TOSHITERU HATAYAMA
Keyword(s):  
Mobile Device

Examining the accuracy of estimated smartphone use: How well-being and usage level predict discrepancies between estimated and actual use

2019 ◽  
Author(s):  
Craig Sewall ◽  
Daniel Rosen ◽  
Todd M. Bear
Keyword(s):  
Public Health ◽  
Social Media ◽  
Life Satisfaction ◽  
Mobile Device ◽  
Screen Time ◽  
Well Being ◽  
Actual Use ◽  
Smartphone Use ◽  
Usage Level

The increasing ubiquity of mobile device and social media (SM) use has generated a substantial amount of research examining how these phenomena may impact public health. Prior studies have found that mobile device and SM use are associated with various aspects of well-being. However, a large portion of these studies relied upon self-reported estimates to measure amount of use, which can be inaccurate. Utilizing Apple’s “Screen Time” application to obtain actual iPhone and SM use data, the current study examined the accuracy of self-reported estimates, how inaccuracies bias relationships between use and well-being (depression, loneliness, and life satisfaction), and the degree to which inaccuracies were predicted by levels of well-being. Among a sample of 393 iPhone users, we found that: a.) participants misestimated their weekly overall iPhone and SM use by 22.1 and 16.6 hours, respectively; b.) the correlations between estimated use and well-being variables were consistently stronger than the correlations between actual use and well-being variables; and c.) the amount of inaccuracy in estimated use is associated with levels of participant well-being as well as amount of use. These findings suggest that estimates of device/SM use may be biased by factors that are fundamental to the relationships being investigated. **This manuscript is currently under review**

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