In this study, we present advances on the development of proactive control for online individual user adaptation in a welfare robot guidance scenario, with the integration of three main modules: navigation control, visual human detection, and temporal error correlation-based neural learning. The proposed control approach can drive a mobile robot to autonomously navigate in relevant indoor environments.
Datsets and scripts are for derivation of a lightweight AIoT malware detection model.
This dataset provides Channel Impulse Response (CIR) measurements from standard-compliant IEEE 802.11ay packets to validate Integrated Sensing and Communication (ISAC) methods. The CIR sequences contain reflections of the transmitted packets on people moving in an indoor environment. They are collected with a 60 GHz software-defined radio experimentation platform based on the IEEE 802.11ay Wi-Fi standard, which is not affected by frequency offsets by operating in full-duplex mode.
The dataset is divided into three parts:
This peach tree disease detection dataset is a multimodal, multi-angle dataset which was constructed for monitoring the growth of peach trees, including stress analysis and prediction. An orchard of peach trees is considered in the area of Thessaly, where 889 peach trees were recorded in a full crop season starting from Jul. 2021 to Sep. 2022. The dataset includes a) aerial / Unmanned Aerial Vehicle (UAV) images, b) ground RGB images/photos, and c) ground multispectral images/photos.
Insecurity is a problem that affects all cities around the world to a greater or lesser extent, and some of them make use of video surveillance to combat it, setting up monitoring centres with hundreds of cameras. For the most part, these centres are staffed by personnel responsible for observation and incident response. The advancement of technology in the market offers the possibility to optimise and add value to these processes.
This Dataset is related to the data prices of the cryptocurrency "Ethereum" over four years, from 01/01/2018 to 11/22/2022. Also, the new dataset created by the predictions of the intelligent filtered LSTM model is included.
Data diversity and volume are crucial to the success of training deep learning models, while in the medical imaging field, the difficulty and cost of data collection and annotation are especially huge. Specifically in robotic surgery, data scarcity and imbalance have heavily affected the model accuracy and limited the design and deployment of deep learning-based surgical applications such as surgical instrument segmentation.
The dataset, Pedestrians with IMU (Ped-IMU), contains pedestrians moving in different behaviors while carrying a wearable sensor. We use a smartphone mounted on a subject's waist as a wearable device and record the smartphone's accelerometer, gyroscope, and magnetometer readings. We invite ten subjects of ages 22-35 to perform walking around with 4 trajectories: Left-Turn Back (L-TB), Left-Go Straight (L-GS), Right-Turn Back (R-TB), and Right-Go Straight (R-GS). A camera is set with screen ratio 16:9, resolution 1920*1080 pixels, and 12 fps.
DRL DP USV
A dataset of Global Positioning System (GPS) spoofing attacks is presented in this article. This dataset includes data extracted from authentic GPS signals collected from different locationsto emulate a moving and a static autonomous vehicle using a universal software radio peripheral unit configured as a GPS receiver. During the data collection, 13 features are extracted from eight-parallel channels at different receiver stages (i.e., acquisition, tracking, and navigation decoding).
