scholarly journals Visual-Inertial Odometry of Smartphone under Manhattan World

2020 ◽  
Vol 12 (22) ◽  
pp. 3818
Author(s):  
YuAn Wang ◽  
Liang Chen ◽  
Peng Wei ◽  
XiangChen Lu
Keyword(s):  
Mobile Phone ◽  
Structural Feature ◽  
Real Time ◽  
Structural Features ◽  
Feature Extraction Method ◽  
The Public ◽  
Window Method ◽  
Tightly Coupled ◽  
Sliding Window Method ◽  
Public Datasets

Based on the hypothesis of the Manhattan world, we propose a tightly-coupled monocular visual-inertial odometry (VIO) system that combines structural features with point features and can run on a mobile phone in real-time. The back-end optimization is based on the sliding window method to improve computing efficiency. As the Manhattan world is abundant in the man-made environment, this regular world can use structural features to encode the orthogonality and parallelism concealed in the building to eliminate the accumulated rotation error. We define a structural feature as an orthogonal basis composed of three orthogonal vanishing points in the Manhattan world. Meanwhile, to extract structural features in real-time on the mobile phone, we propose a fast structural feature extraction method based on the known vertical dominant direction. Our experiments on the public datasets and self-collected dataset show that our system is superior to most existing open-source systems, especially in the situations where the images are texture-less, dark, and blurry.

scholarly journals The Necessity of Mobile Phone Technologies for Public Health Surveillance in Benin

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Yaovi M. G. Hounmanou ◽  
Murielle S. S. Agonsanou ◽  
Victorien Dougnon ◽  
Mahougnon H. B. Vodougnon ◽  
Ephraim M. Achoh ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Smart Phone ◽  
Cross Sectional Study ◽  
Health Surveillance ◽  
Medical Professionals ◽  
Surveillance Systems ◽  
Cross Sectional ◽  
The Public ◽  
Phone Calls

A cross-sectional study was conducted in March 2016 to assess the need of mobile phone technologies for health surveillance and interventions in Benin. Questionnaires were administered to 130 individuals comprising 25 medical professionals, 33 veterinarians, and 72 respondents from the public. All respondents possess cell phones and 75%, 84%, and 100% of the public, medical professionals, and veterinarians, respectively, generally use them for medical purposes. 75% of respondents including 68% of medics, 84.8% of veterinarians, and 72.2% of the public acknowledged that the current surveillance systems are ineffective and do not capture and share real-time information. More than 92% of the all respondents confirmed that mobile phones have the potential to improve health surveillance in the country. All respondents reported adhering to a nascent project of mobile phone-based health surveillance and confirmed that there is no existing similar approach in the country. The most preferred methods by all respondents for effective implementation of such platform are phone calls (96.92%) followed by SMS (49.23%) and smart phone digital forms (41.53%). This study revealed urgent needs of mobile phone technologies for health surveillance and interventions in Benin for real-time surveillance and efficient disease prevention.

scholarly journals SLAM: A Malware Detection Method Based on Sliding Local Attention Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jun Chen ◽  
Shize Guo ◽  
Xin Ma ◽  
Haiying Li ◽  
Jinhong Guo ◽  
...  
Keyword(s):  
Language Processing ◽  
Semantic Information ◽  
Attention Mechanism ◽  
Malicious Code ◽  
Feature Extraction Method ◽  
Structure Information ◽  
Execution Sequence ◽  
Window Method ◽  
Great Progress ◽  
Sliding Window Method

Since the number of malware is increasing rapidly, it continuously poses a risk to the field of network security. Attention mechanism has made great progress in the field of natural language processing. At the same time, there are many research studies based on malicious code API, which is also like semantic information. It is a worthy study to apply attention mechanism to API semantics. In this paper, we firstly study the characters of the API execution sequence and classify them into 17 categories. Secondly, we propose a novel feature extraction method based on API execution sequence according to its semantics and structure information. Thirdly, based on the API data characteristics and attention mechanism features, we construct a detection framework SLAM based on local attention mechanism and sliding window method. Experiments show that our model achieves a better performance, which is a higher accuracy of 0.9723.

scholarly journals MultiRPN-DIDNet: Multiple RPNs and Distance-IoU Discriminative Network for Real-Time UAV Target Tracking

2021 ◽  
Vol 13 (14) ◽  
pp. 2772
Author(s):  
Li Zhuo ◽  
Bin Liu ◽  
Hui Zhang ◽  
Shiyu Zhang ◽  
Jiafeng Li
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Tracking Performance ◽  
The Public ◽  
Tracking Tasks ◽  
Bounding Box ◽  
Single Target ◽  
Aerial Vehicle ◽  
Public Datasets ◽  
Single Target Tracking

Target tracking in low-altitude Unmanned Aerial Vehicle (UAV) videos faces many technical challenges due to the relatively small sizes, various orientation changes of the objects and diverse scenes. As a result, the tracking performance is still not satisfactory. In this paper, we propose a real-time single-target tracking method with multiple Region Proposal Networks (RPNs) and Distance-Intersection-over-Union (Distance-IoU) Discriminative Network (DIDNet), namely MultiRPN-DIDNet, in which ResNet50 is used as the backbone network for feature extraction. Firstly, an instance-based RPN suitable for the target tracking task is constructed under the framework of Simases Neural Network. RPN is to perform bounding box regression and classification, in which channel attention mechanism is integrated to improve the representative capability of the deep features. The RPNs built on the Block 2, Block 3 and Block 4 of ResNet50 output their own Regression (Reg) coefficients and Classification scores (Cls) respectively, which are weighted and then fused to determine the high-quality region proposals. Secondly, a DIDNet is designed to correct the candidate target’s bounding box finely through the fusion of multi-layer features, which is trained with the Distance-IoU loss. Experimental results on the public datasets of UAV20L and DTB70 show that, compared with the state-of-the-art UAV trackers, the proposed MultiRPN-DIDNet can obtain better tracking performance with fewer region proposals and correction iterations. As a result, the tracking speed has reached 33.9 frames per second (FPS), which can meet the requirements of real-time tracking tasks.

scholarly journals Simulation and Detection of Small Drones/Suspicious UAVs in Drone Grid

2021 ◽  
Vol 9 (VI) ◽  
pp. 5452-5458
Author(s):  
Arpit Gupta
Keyword(s):  
Real Time ◽  
High Speed ◽  
Visual Detection ◽  
Autonomous Systems ◽  
Sliding Window ◽  
The Past ◽  
Detection And Tracking ◽  
Window Method ◽  
Sliding Window Method ◽  
State Of Art

Today’s technology is evolving towards autonomous systems and the demand in autonomous drones, cars, robots, etc. has increased drastically in the past years. This project presents a solution for autonomous real-time visual detection and tracking of hostile drones by moving cameras equipped on surveillance drones. The algorithm developed in this project, based on state-of-art machine learning and computer vision methods, succeeds at autonomously detecting and tracking a single drone by moving a camera and can run at real-time. The project can be divided into two main parts: the detection and the tracking. The detection is based on the YOLOv3 (You Only Look Once v3) algorithm and a sliding window method. The tracking is based on the GOTURN (Generic Object Tracking Using Regression Networks) algorithm, which allows the tracking of generic objects at high speed. In order to allow autonomous tracking and enhance the accuracy, a combination of GOTURN and tracking by detection using YOLOv3 was developed.

Real-time dense map fusion for stereo SLAM

Robotica ◽  
2018 ◽  
Vol 36 (10) ◽  
pp. 1510-1526 ◽  
Author(s):  
Taihú Pire ◽  
Rodrigo Baravalle ◽  
Ariel D'Alessandro ◽  
Javier Civera
Keyword(s):  
Real Time ◽  
3D Model ◽  
Field Of View ◽  
Single View ◽  
The Public ◽  
Starting Position ◽  
System Use ◽  
Public Datasets ◽  
Short Time

SUMMARYA robot should be able to estimate an accurate and dense 3D model of its environment (a map), along with its pose relative to it, all of it in real time, in order to be able to navigate autonomously without collisions.As the robot moves from its starting position and the estimated map grows, the computational and memory footprint of a dense 3D map increases and might exceed the robot capabilities in a short time. However, a global map is still needed to maintain its consistency and plan for distant goals, possibly out of the robot field of view.In this work, we address such problem by proposing a real-time stereo mapping pipeline, feasible for standard CPUs, which is locally dense and globally sparse and accurate. Our algorithm is based on a graph relating poses and salient visual points, in order to maintain a long-term accuracy with a small cost. Within such framework, we propose an efficient dense fusion of several stereo depths in the locality of the current robot pose.We evaluate the performance and the accuracy of our algorithm in the public datasets of Tsukuba and KITTI, and demonstrate that it outperforms single-view stereo depth. We release the code as open-source, in order to facilitate the system use and comparisons.

Design of Portable Electrocardiograph Monitoring System

2020 ◽  
Author(s):  
Jia Hua-Ping ◽  
Zhao Jun-Long ◽  
Liu Jun
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Data Storage ◽  
P Wave ◽  
Ecg Monitoring ◽  
The Public ◽  
Ecg Signals ◽  
Early Warning Mechanism ◽  
Timely Treatment ◽  
Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

scholarly journals The case for tracking misinformation the way we track disease

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110138
Author(s):  
Erika Bonnevie ◽  
Jennifer Sittig ◽  
Joe Smyser
Keyword(s):  
Public Health ◽  
Real Time ◽  
Supervised Machine Learning ◽  
Disease Spread ◽  
The Public ◽  
Monitoring Tools ◽  
Core Function ◽  
Public Health Organizations ◽  
Front Line Workers ◽  
Media Data

While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public’s health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise this process daily, and provide insights to practitioners across a range of disciplines. Through this work, we have gleaned three lessons to address misinformation. (1) Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. (2) Existing systems can identify and track threats against health experts and institutions, which have been subject to unprecedented harassment. This supports their safety and helps prevent the further erosion of trust in public institutions. (3) Responses to misinformation should draw from cross-sector crisis management best practices and address coordination gaps. Real-time monitoring and addressing misinformation should be a core function of public health, and public health should be a core use case for data scientists developing monitoring tools. The tools to accomplish these tasks are available; it remains up to us to prioritize them.

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