The frequency domain measurement of the scattering parameter, S21, of the wireless channel was carried out using the ZVB14 Vector Network Analyzer (VNA) from Rhode and Schwartz. The measurement system consists of the VNA, low loss RF cables, and omnidirectional antennas at the transmitter and receiver ends. The transmitter and receiver heights were fixed at 1.5 m. A program script was written for the VNA to measure 10 consecutive sweeps: each sweep contains 601 frequency sample points with spacing of 0.167 MHz to cover a 100 MHz band centered at 2.4 GHz.
This RSSI Dataset is a comprehensive set of Received Signal Strength Indicator (RSSI) readings gathered from within three different types of scenarios. Three wireless technologies were used which consisted of:
WiFi (IEEE 802.11n 2.4GHz band)
Bluetooth Low Energy (BLE) and
Zigbee (IEEE 802.15.4)
For the experimentation, the equipment utilized consisted of Raspberry Pi 3 Model Bs, Gimbal Series 10 Beacons, and Series 2 Xbees with Arduino Uno microcontrollers.
Mp3 is a very popular audio format and hence it can be a good host for carrying hidden messages. Therefore, different steganography methods have been proposed for mp3 hosts. UnderMp3Cover is one of such algorithms and has some important advantage over other comparable methods. First, the popular steganography method mp3stego, works directly on non-compressed samples. Therefore, using covers that have been compressed before could lead to serious degradation of its security. UnderMp3Cover does not have this important limitation.
This database consists of the data used for the 2018 IEEE Signal Processing Cup. This iteration of the Signal Processing Cup was a forensic camera model identification challenge. Teams of undergraduate students were tasked with building a system capable of determining type of camera (manufacturer and model) that captured a digital image without relying on metadata.
The system obtained interference information from the measurement signal, solved the problem of phase wrapping, and got the accurate coordinates of target. The tags and tag-free items including shrimp chips, cola and instant noodles were taken as target respectively in experiment.
The RSSI-Dataset provides a comprehensive set of Received Signal Strength Indication (RSSI) readings from within two indoor office buildings. Four wireless technologies were used:
Zigbee (IEEE 802.15.4),
WiFi (IEEE 802. 11),
Bluetooth Low Energy (BLE) and
Long Range Area-Wide Network (LoRaWAN).
For experimentation Arduinos Raspberry Pi, XBees, Gimbal beacons Series 10 and Dragino LoRa Shield were also used.
These .s2p files contain the S-parameters measured between two on-neck antennas for multiple test subjects acting out four activites. Each files is one trial of measurement, containing 20 seconds of data sampled at 200 Hz.
Visual tracking methods have achieved a successful development in recent years. Especially the Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. The advancement in DCF tracking performance is predominantly attributed to powerful features and sophisticated online learning formulations. However, it would come to some troubles if the tracker learns the samples indiscriminately.
