Mobile Device to Measure Ubiquitous Danger in a Great City Based on Cultural Algorithms

2012 ◽  
pp. 647-655
Author(s):  
Alberto Ochoa ◽  
Erick Trejo ◽  
Daniel Azpeitia ◽  
Néstor Esquinca ◽  
Rubén Jaramillo ◽  
...  
Keyword(s):  
Mobile Device ◽  
Cultural Algorithms ◽  
Great City

Implementation of a Mobile Device to Determine Danger in a Great City Based on Ubiquity

2012 ◽  
Author(s):  
Alberto Ochoa ◽  
Julio Ponce ◽  
Jons S'nchez ◽  
Arturo Hern'ndez ◽  
Alice Makalawele
Keyword(s):  
Mobile Device ◽  
Great City

scholarly journals Mobile device to measure the danger of ubiquity in a great city

2012 ◽  
Vol 35 ◽  
pp. 92-100
Author(s):  
Erick Trejo ◽  
Alberto Ochoa ◽  
Néstor Esquinca ◽  
Saúl González
Keyword(s):  
Mobile Device ◽  
Great City

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

Monumental Gateway to a Great City

1912 ◽  
Vol 107 (23) ◽  
pp. 484-489
Keyword(s):  
Great City

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.

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