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IMU-Based 6-DOF Odometry
Matlab scripts to create readable, color and high contrast figures for publications
We develop an acoustic thermometer empowered by a single smartphone
AI-IMU Dead-Reckoning
Collect and classify android open source projects 微信公众号:codekk
Android Sensor Orientation Library helps you to get more accurate vector values of orientation, using all available device sensors.
Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
Awesome Lists for Tenure-Track Assistant Professors and PhD students. (助理教授/博士生生存指南)
Android application that uses the barometer to predict the current altitude and a building's floor level
The lack of benchmarking datasets for pedestrian stride length estimation makes it hard to pinpoint differences of published methods. Existing datasets either lack the ground-truth of each stride or are limited to small spaces with single scene or motion pattern. To fully evaluate the performance of proposed ASLE algorithm, we conducted benchmark dataset for natural pedestrian dead reckoning using smartphone sensors and FM-INS module. we leveraged the FM-INS module to provide the ground-truth of each stride with motion distance errors in 0.3% of the entire travel distance. The datasets were obtained from a group of healthy adults with natural motion patterns (fast walking, normal walking, slow walking, running, jumping). The datasets contained more than 22 km, 10000 strides of gait measurements. The datasets cover both indoor and outdoor cases, including: stairs, escalators, elevators, office environments, shopping mall, streets and metro station. To maximize compatibility, all data is published in open and simple file formats. The sensor is sampled at 100 Hz. Throughout the datasets, the users hold the phone in their hand in front of their chest. The samples hold nine degree-of-freedom sensor data and the corresponding stride number, stride length and total walking distance.
BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration
CamComSim is a LED-to-camera communication simulator
android camera calibration, undistort安卓相机标定,畸变校正
Dynamic Programming
Contrastive Learning (SimCLR) for Human Activity Recognition
Android 开发中的日常积累
Codes for Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones
human's walking pose context classification, gps/ins system navigation error compensation
Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic and Beyond
Convolutional Neural Networks for Denoising Gyroscopes of Low-Cost IMUs
Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.
:whale2: 大学《数据结构(C语言版)(第2版)》 严蔚敏版的配套PPT/源代码/实验安排/课时安排
An extended Kalman filter for magnetic field SLAM
See https://code.google.com/p/fastdtw. I've added a pom.xml to create a jar file.
Thanks to the proliferation of smart devices, such as smartphones and wearables, which are equipped with computation, communication and sensing capabilities, a plethora of new location-based services and applications are available for the users at any time and everywhere. Understanding human mobility has gain importance to offer better services able to provide valuable products to the user whenever it's required. The ability to predict when and where individuals will go next allows enabling smart recommendation systems or a better organization of resources such as public transport vehicles or taxis. Network providers can predict future activities of individuals and groups to optimize network handovers, while transport systems can provide more vehicles or lines where required, reducing waiting time and discomfort to their clients. The representation of the movements of individuals or groups of mobile entities are called human mobility models. Such models replicate real human mobility characteristics, enabling to simulate movements of different individuals and infer their future whereabouts. The development of these models requires to collect in a centralized location, as a server, the information related to the users' locations. Such data represents sensitive information, and the collection of those threatens the privacy of the users involved. The recent introduction of federated learning, a privacy-preserving approach to build machine and deep learning models, represents a promising technique to solve the privacy issue. Federated learning allows mobile devices to contribute with their private data to the model creation without sharing them with a centralized server. In this thesis, we investigate the application of the federated learning paradigm to the field of human mobility modelling. Using three different mobility datasets, we first designed and developed a robust human mobility model by investigating different classes of neural networks and the influence of demographic data over models' performance. Second, we applied federated learning to create a human mobility model based on deep learning which does not require the collection of users' mobility traces, achieving promising results on two different datasets. Users' data remains so distributed over the big number of devices which have generated them, while the model is shared and trained among the server and the devices. Furthermore, the developed federated model has been the subject of different analyses including: the effects of sparse availability of the clients; The communication costs required by federated settings; The application of transfer-learning techniques and model refinement through federated learning and, lastly, the influence of differential privacy on the model’s prediction performance, also called utility
We wirte a filtflit function in java . The filtflit's output is the same as it's in Matlab .
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.