scholarly journals A spheroid toxicity assay using magnetic 3D bioprinting and real-time mobile device-based imaging

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Hubert Tseng ◽  
Jacob A. Gage ◽  
Tsaiwei Shen ◽  
William L. Haisler ◽  
Shane K. Neeley ◽  
...  
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
3D Bioprinting ◽  
Toxicity Assay

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.

scholarly journals Real-Time Visual Place Recognition for Personal Localization on a Mobile Device

2017 ◽  
Vol 97 (1) ◽  
pp. 213-244 ◽  
Author(s):  
Michał R. Nowicki ◽  
Jan Wietrzykowski ◽  
Piotr Skrzypczyński
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Place Recognition ◽  
Visual Place Recognition

A Real-Time In-Air Signature Biometric Technique Using a Mobile Device Embedding an Accelerometer

2010 ◽  
pp. 497-503 ◽  
Author(s):  
J. Guerra Casanova ◽  
C. Sánchez Ávila ◽  
A. de Santos Sierra ◽  
G. Bailador del Pozo ◽  
V. Jara Vera
Keyword(s):  
Real Time ◽  
Mobile Device

Know Your World Better

2018 ◽  
pp. 777-793
Author(s):  
Srinivasa K. G. ◽  
Satvik Jagannath ◽  
Aakash Nidhi
Keyword(s):  
Information Retrieval ◽  
Augmented Reality ◽  
Real Time ◽  
Mobile Devices ◽  
Mobile Device ◽  
Sensitive Information ◽  
Context Sensitive ◽  
Close Proximity ◽  
Augmented Reality Application ◽  
Device User

Mobile devices are changing the way people live. Users have everything on their fingertips and to support them, there are scores of application which add to the usability and comfort. “Know your world better” is an Augmented Reality application developed for Android. This application helps the user to find friends and locate places in close proximity. In this paper we talk about an application that describes a method of augmenting Point of Interests (POI's) on a mobile device. User has to move his phone pointing in a direction of his choice and POI's if any are shown in real time. The user's interest with respect to the environment is inferred from speech or by selecting from the choices; this data is used for information retrieval from the cloud. The result of context-sensitive information retrieval is augmented onto the view of the mobile and provides speech output.

UbiWave

2010 ◽  
pp. 124-179
Author(s):  
Fan Wu ◽  
Emmanuel Agu ◽  
Clifford Lindsay ◽  
Chung-han Chen
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Mobile Device ◽  
Unequal Error Protection ◽  
New Class ◽  
Mobile Access ◽  
Mobile Graphics ◽  
Error Metric ◽  
Battery Energy ◽  
Visual Realism

Advances in ubiquitous displays and wireless communications have fueled the emergence of exciting mobile graphics applications including 3D virtual product catalogs, 3D maps, security monitoring systems and mobile games. Current trends that use cameras to capture geometry, material re?ectance and other graphics elements mean that very high resolution inputs are accessible to render extremely photorealistic scenes. However, captured graphics content can be many gigabytes in size, and must be simpli?ed before they can be used on small mobile devices, which have limited resources, such as memory, screen size and battery energy. Scaling and converting graphics content to a suitable rendering format involves running several software tools, and selecting the best resolution for target mobile device is often done by trial and error, which all takes time. Wireless errors can also affect transmitted content and aggressive compression is needed for low-bandwidth wireless networks. Most rendering algorithms are currently optimized for visual realism and speed, but are not resource or energy ef?cient on a mobile device. This chapter focuses on the improvement of rendering performance by reducing the impacts of these problems with UbiWave, an end-to-end framework to enable real time mobile access to high resolution graphics using wavelets. The framework tackles the issues including simpli?cation, transmission, and resource ef?cient rendering of graphics content on mobile device based on wavelets by utilizing 1) a Perceptual Error Metric (PoI) for automatically computing the best resolution of graphics content for a given mobile display to eliminate guesswork and save resources, 2) Unequal Error Protection (UEP) to improve the resilience to wireless errors, 3) an Energy-ef?cient Adaptive Real-time Rendering (EARR) heuristic to balance energy consumption, rendering speed and image quality and 4) an Energy-ef?cient Streaming Technique. The results facilitate a new class of mobile graphics application which can gracefully adapt the lowest acceptable rendering resolution to the wireless network conditions and the availability of resources and battery energy on mobile device adaptively.

scholarly journals The Real-Time Mobile Application for Classifying of Endangered Parrot Species Using the CNN Models Based on Transfer Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Daegyu Choe ◽  
Eunjeong Choi ◽  
Dong Keun Kim
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Mobile Device ◽  
Mobile Application ◽  
Feature Recognition ◽  
Species Recognition ◽  
Color Similarity ◽  
Parrot Species ◽  
The Many ◽  
Google Search

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

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