Real-time localization of mobile device by filtering method for sensor fusion

2017 ◽  
Author(s):  
Takashi Fuse ◽  
Keita Nagara
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Mobile Device ◽  
Filtering Method

Bayesian Spam Detection Framework on Mobile Device

2019 ◽  
Vol 12 ◽  
Author(s):  
Yitao Yang ◽  
Guozi Sun ◽  
Chengyan Qiu
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Bayesian Classification ◽  
Classification Algorithm ◽  
Spam Detection ◽  
Spam Filtering ◽  
Learning Mechanism ◽  
The Real ◽  
Filtering Method ◽  
Feedback Learning

In recent years, the spam message problem becomes more serious. Similar to spam mail, the spam message in phone brings a big trouble to users. Bayesian classification algorithm, which is simple to design and has the higher accuracy, becomes the most effective filtration methods. Bayesian classification algorithm, which is simple to design and has the higher accuracy, becomes the most effective filtering method. A Bayesian spam detection framework is designed in the paper and is deployed on Android device to test. Besides it can filtering coming messages and classify them into normal or spam in real time, it introduces feedback learning mechanism to make its result more accurate. The experiments are conducted under the real environment. The results show that the framework can meet the requirement of spam filtering.

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.

Sequential Sensor Fusion-Based Real-Time LSTM Gait Pattern Controller for Biped Robot

2021 ◽  
Vol 21 (2) ◽  
pp. 2241-2255 ◽  
Author(s):  
Tzuu-Hseng S. Li ◽  
Ping-Huan Kuo ◽  
Chuan-Han Cheng ◽  
Chia-Ching Hung ◽  
Po-Chien Luan ◽  
...  
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Biped Robot ◽  
Gait Pattern

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

scholarly journals Three Revised Kalman Filtering Models for Short-Term Rail Transit Passenger Flow Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Pengpeng Jiao ◽  
Ruimin Li ◽  
Tuo Sun ◽  
Zenghao Hou ◽  
Amir Ibrahim
Keyword(s):  
Real Time ◽  
Nonparametric Regression ◽  
Kalman Filtering ◽  
Rail Transit ◽  
Correction Coefficient ◽  
Short Term ◽  
Transit System ◽  
Filtering Method ◽  
Passenger Flow ◽  
Model Based

Short-term prediction of passenger flow is very important for the operation and management of a rail transit system. Based on the traditional Kalman filtering method, this paper puts forward three revised models for real-time passenger flow forecasting. First, the paper introduces the historical prediction error into the measurement equation and formulates a revised Kalman filtering model based on error correction coefficient (KF-ECC). Second, this paper employs the deviation between real-time passenger flow and corresponding historical data as state variable and presents a revised Kalman filtering model based on Historical Deviation (KF-HD). Third, the paper integrates nonparametric regression forecast into the traditional Kalman filtering method using a Bayesian combined technique and puts forward a revised Kalman filtering model based on Bayesian combination and nonparametric regression (KF-BCNR). A case study is implemented using statistical passenger flow data of rail transit line 13 in Beijing during a one-month period. The reported prediction results show that KF-ECC improves the applicability to historical trend, KF-HD achieves excellent accuracy and stability, and KF-BCNR yields the best performances. Comparisons among different periods further indicate that results during peak periods outperform those during nonpeak periods. All three revised models are accurate and stable enough for on-line predictions, especially during the peak periods.

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