wq1989 Goto Github PK

6-dof-inertial-odometry

IMU-Based 6-DOF Odometry

academicfigures

Matlab scripts to create readable, color and high contrast figures for publications

accelerometer-signal-recognition-using-resnet-18-and-modified-recurrence-plots

acute

We develop an acoustic thermometer empowered by a single smartphone

adaptiv

ai-imu-dr

AI-IMU Dead-Reckoning

android-open-project

Collect and classify android open source projects 微信公众号：codekk

android-orientation-sensor

Android Sensor Orientation Library helps you to get more accurate vector values of orientation, using all available device sensors.

androidtensorflowmachinelearningexample

Android TensorFlow MachineLearning Example (Building TensorFlow for Android)

awesome_lists

Awesome Lists for Tenure-Track Assistant Professors and PhD students. (助理教授/博士生生存指南)

barometeraltitudefloorprediction

Android application that uses the barometer to predict the current altitude and a building's floor level

benchmark-dataset-for-adaptive-stride-length-estimation

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

BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration

cage4har

camcomsim

CamComSim is a LED-to-camera communication simulator

cameracalibration

android camera calibration, undistort安卓相机标定，畸变校正

contourextraction

Dynamic Programming

contrastivelearninghar

Contrastive Learning (SimCLR) for Human Activity Recognition

corelink

Android 开发中的日常积累

deep-learning

deep-speed-constrained-ins

Codes for Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones

deeplearning-aided-smartphone-pdr

human's walking pose context classification, gps/ins system navigation error compensation

deepsocial

Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic and Beyond

denoise-imu-gyro

Convolutional Neural Networks for Denoising Gyroscopes of Low-Cost IMUs

ds_yanweimin-

: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

ekfmagslam

An extended Kalman filter for magnetic field SLAM

fastdtw

See https://code.google.com/p/fastdtw. I've added a pom.xml to create a jar file.

federated-learning-for-human-mobility-models

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

filtfilt

We wirte a filtflit function in java . The filtflit's output is the same as it's in Matlab .

find

High-precision indoor positioning framework for most wifi-enabled devices.