Experimental Approach of the Asymptotic Computational Complexity of Shaders for Mobile Devices with OpenGL ES

2014 ◽  
Author(s):  
Alex de Souza Campelo Lima ◽  
Edson Alves da Costa
Keyword(s):  
Computational Complexity ◽  
Mobile Devices ◽  
Experimental Approach ◽  
Opengl Es

scholarly journals Experimental Approach of the Asymptotic Computational Complexity of Shaders for Mobile Devices with OpenGL ES

2017 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Alex S. C. Lima ◽  
Edson A. C. Junior
Keyword(s):  
Computational Complexity ◽  
Least Squares ◽  
Mobile Devices ◽  
Experimental Approach ◽  
Critical Factor ◽  
Sampled Data ◽  
Method Of Least Squares ◽  
Opengl Es ◽  
Automatic Tool

The usage of mobile devices and increasingly realistic graphics is emerging, but the graphics performance is still a critical factor in games. There’s more hardware restriction on mobile devices than on a computer. Thus, this paper proposes an experimental approximation of the asymptotic computational complexity of miscellaneous vertex and fragment shaders for Android and iOS platforms. The asymptotic complexities of the shaders will be analyzed based on number of instructions per second and rendering time metrics, depending on the number of polygons rendered. By means of the adjusted curves is also possible to compare the performance of the devices used in this work, which are the Nexus 4, HTC One, iPhone 5s and iPad Air. Besides, an automatic tool – that plots the data and uses the method of least squares to adjust the values obtained – will be presented, being able to estimate which curve has better approximation to the sampled data.

scholarly journals User-independent accelerometer-based gesture recognition for mobile devices

2013 ◽  
Vol 1 (3) ◽  
pp. 11-25 ◽  
Author(s):  
Xian Wang ◽  
Paula Tarrío ◽  
Ana María Bernardos ◽  
Eduardo Metola ◽  
José Ramón Casar
Keyword(s):  
Computational Complexity ◽  
Mobile Devices ◽  
Gesture Recognition ◽  
Inertial Sensors ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Smart Phone ◽  
Recognition System ◽  
Independent Manner ◽  
Wheeled Robot

Many mobile devices embed nowadays inertial sensors. This enables new forms of human-computer interaction through the use of gestures (movements performed with the mobile device) as a way of communication. This paper presents an accelerometer-based gesture recognition system for mobile devices which is able to recognize a collection of 10 different hand gestures. The system was conceived to be light and to operate in a user-independent manner in real time. The recognition system was implemented in a smart phone and evaluated through a collection of user tests, which showed a recognition accuracy similar to other state-of-the art techniques and a lower computational complexity. The system was also used to build a human-robot interface that enables controlling a wheeled robot with the gestures made with the mobile phone

scholarly journals A Computationally Inexpensive Energy Model for Horizontal Electric Water Heaters With Scheduling

2019 ◽  
Author(s):  
Philip Nel ◽  
MJ (Thinus) Booysen ◽  
Brink van der Merwe
Keyword(s):  
Energy Consumption ◽  
Computational Complexity ◽  
Mobile Devices ◽  
Large Scale ◽  
Water Usage ◽  
Schedule Control ◽  
Water Heaters ◽  
Horizontal Orientation ◽  
Key Features ◽  
Large Scale Simulations

Electric water heaters (EWHs) remain one of the main contributors to energy consumption in countries where they are used. EWH models serve as a step towards achieving optimised control, and can also be used to inform users of expected savings due to changes, if the model is energy-based. Various models have been proposed, but none of them include more than half of the six key features that the model presented in this paper supports: horizontal orientation; schedule control; low computational complexity; validation of the model; multinodal stratification; and multinodal standing losses. The presented model is validated against six datasets: four comprising 900 hours with multiple water usage events; and two with only standing losses. The results show that the model estimates energy consumption over ten days including usage with an error of less than 2% and 5% for schedule control and thermostat control respectively. The simulation model is simple enough to execute ten days of simulation in less than 100 milliseconds on a standard desktop machine, 150 times faster than a prominent model from literature, making it also suitable for large scale simulations or for use on mobile devices.

scholarly journals Real-Time Facial Affective Computing on Mobile Devices

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 870 ◽  
Author(s):  
Yuanyuan Guo ◽  
Yifan Xia ◽  
Jing Wang ◽  
Hui Yu ◽  
Rung-Ching Chen
Keyword(s):  
Computational Complexity ◽  
Real Time ◽  
Mobile Devices ◽  
High Performance ◽  
Affective Computing ◽  
State Of The Art ◽  
Light Weight ◽  
Computational Power ◽  
Art Methods ◽  
And Storage

Convolutional Neural Networks (CNNs) have become one of the state-of-the-art methods for various computer vision and pattern recognition tasks including facial affective computing. Although impressive results have been obtained in facial affective computing using CNNs, the computational complexity of CNNs has also increased significantly. This means high performance hardware is typically indispensable. Most existing CNNs are thus not generalizable enough for mobile devices, where the storage, memory and computational power are limited. In this paper, we focus on the design and implementation of CNNs on mobile devices for real-time facial affective computing tasks. We propose a light-weight CNN architecture which well balances the performance and computational complexity. The experimental results show that the proposed architecture achieves high performance while retaining the low computational complexity compared with state-of-the-art methods. We demonstrate the feasibility of a CNN architecture in terms of speed, memory and storage consumption for mobile devices by implementing a real-time facial affective computing application on an actual mobile device.

scholarly journals Trip mode inference from mobile phone signaling data using Logarithm Gaussian Mixture Model

2020 ◽  
Vol 13 (1) ◽  
pp. 429-445
Author(s):  
Xiaoxu Chen ◽  
Xiangdong Xu ◽  
Chao Yang
Keyword(s):  
Computational Complexity ◽  
Mobile Phone ◽  
Travel Time ◽  
Mobile Devices ◽  
Mixed Model ◽  
Gaussian Mixture ◽  
High Accuracy ◽  
Gps Data ◽  
Inference Method ◽  
Acceptable Accuracy

Trip mode inference plays an important role in transportation planning and management. Most studies in the field have focused on the methods based on GPS data collected from mobile devices. While these methods can achieve relatively high accuracy, they also have drawbacks in data quantity, coverage, and computational complexity. This paper develops a trip mode inference method based on mobile phone signaling data. The method mainly consists of three parts: activity-nodes recognition, travel-time computation, and clustering using the Logarithm Gaussian Mixed Model. Moreover, we compare two other methods (i.e., Gaussian Mixed Model and K-Means) with the Logarithm Gaussian Mixed Model. We conduct experiments using real mobile phone signaling data in Shanghai and the results show that the proposed method can obtain acceptable accuracy overall. This study provides an important opportunity to infer trip mode from the aspect of probability using mobile phone signaling data.

An Adaptive LOD Setting Methodology with OpenGL ES Library on Mobile Devices

2014 ◽  
Author(s):  
Jin-Chun Piao ◽  
Chang-Woo Cho ◽  
Cheong-Ghil Kim ◽  
Bernd Burgstaller ◽  
Shin-Dug Kim
Keyword(s):  
Mobile Devices ◽  
Opengl Es

Experimental Determination of the Effective Resolution Limit in Thick Specimens at High Voltages

1975 ◽  
Vol 33 ◽  
pp. 664-665
Author(s):  
Mircea Fotino
Keyword(s):  
Experimental Approach ◽  
Chromatic Aberration ◽  
Resolving Power ◽  
Biological Research ◽  
Specimen Thickness ◽  
Resolution Limit ◽  
High Voltage Electron Microscopy ◽  
Voltage Electron ◽  
Resolution Level

The use of thick specimens (0.5 μm to 5.0 μm or more) is one of the most resourceful applications of high-voltage electron microscopy in biological research. However, the energy loss experienced by the electron beam in the specimen results in chromatic aberration and thus in a deterioration of the effective resolving power. This sets a limit to the maximum usable specimen thickness when investigating structures requiring a certain resolution level.An experimental approach is here described in which the deterioration of the resolving power as a function of specimen thickness is determined. In a manner similar to the Rayleigh criterion in which two image points are considered resolved at the resolution limit when their profiles overlap such that the minimum of one coincides with the maximum of the other, the resolution attainable in thick sections can be measured by the distance from minimum to maximum (or, equivalently, from 10% to 90% maximum) of the broadened profile of a well-defined step-like object placed on the specimen.

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