Chaganti et al., 2014 - Google Patents
Are narrowband wireless on-body networks wide-sense stationary?Chaganti et al., 2014
- Document ID
- 6321008356802589874
- Author
- Chaganti V
- Hanlen L
- Smith D
- Publication year
- Publication venue
- IEEE Transactions on Wireless Communications
External Links
Snippet
Using narrowband wireless On-Body Area Network (BAN) channel measurements (50 million data points) in diverse environments with multiple subjects, we examine the stationarity of the channel. Wide-Sense Stationarity (WSS) tests and power spectral …
- 230000003595 spectral 0 abstract description 24
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ding et al. | A WiFi-based smart home fall detection system using recurrent neural network | |
JP7154295B2 (en) | Motion detection based on machine learning of radio signal characteristics | |
US11902857B2 (en) | Handling concept drift in Wi-Fi-based localization | |
Kaltiokallio et al. | Follow@ grandma: Long-term device-free localization for residential monitoring | |
Wang et al. | Device-free human activity recognition using commercial WiFi devices | |
Di Domenico et al. | Trained-once device-free crowd counting and occupancy estimation using WiFi: A Doppler spectrum based approach | |
Geng et al. | Enlighten wearable physiological monitoring systems: On-body rf characteristics based human motion classification using a support vector machine | |
Arshad et al. | Wi-chase: A WiFi based human activity recognition system for sensorless environments | |
Smith et al. | Propagation models for body-area networks: A survey and new outlook | |
Chaganti et al. | Are narrowband wireless on-body networks wide-sense stationary? | |
Oestges et al. | Experimental characterization and modeling of outdoor-to-indoor and indoor-to-indoor distributed channels | |
Forbes et al. | Wifi-based human activity recognition using Raspberry Pi | |
Chowdhury | Using Wi-Fi channel state information (CSI) for human activity recognition and fall detection | |
Di Domenico et al. | WiFi-based through-the-wall presence detection of stationary and moving humans analyzing the doppler spectrum | |
Zhu et al. | NotiFi: A ubiquitous WiFi-based abnormal activity detection system | |
Cheng et al. | Device-free human activity recognition based on GMM-HMM using channel state information | |
Liu et al. | A research on CSI-based human motion detection in complex scenarios | |
Al Kalaa et al. | Characterizing the 2.4 GHz spectrum in a hospital environment: Modeling and applicability to coexistence testing of medical devices | |
Guo et al. | TWCC: A robust through-the-wall crowd counting system using ambient WiFi signals | |
Natarajan et al. | A machine learning approach to passive human motion detection using WiFi measurements from commodity IoT devices | |
Mohamed et al. | Characterization of dynamic wireless body area network channels during walking | |
Bernaola et al. | Ensemble learning for seated people counting using WiFi signals: Performance study and transferability assessment | |
Banitalebi-Dehkordi et al. | Compressive-sampling-based positioning in wireless body area networks | |
Al Kalaa et al. | Long term spectrum survey of the 2.4 GHz ISM band in multiple hospital environments | |
Al Kalaa et al. | Estimating the likelihood of wireless coexistence using logistic regression: Emphasis on medical devices |