@article{khan_venkatnarayan_shahzad_2023, title={Using RF Signals to Generate Indoor Maps}, volume={19}, ISSN={["1550-4867"]}, DOI={10.1145/3534121}, abstractNote={Generating maps of indoor environments beyond the line-of-sight finds applications in several areas such as planning, navigation, and security. While researchers have previously explored the use of RF signals to generate maps, prior work has two important limitations: (i) it requires moving the mapping setup along the entire lengths of the sides of the building, and (ii) it generates maps that are not fully connected, rather are scatter plots of locations from where some obstacles reflected the signals. Thus, prior approaches require human interpretation to locate the walls and determine how they merge. In this article, we address these limitations and propose RFMap, which generates fully connected maps, and does not require the measurement setup to be moved along the sides of the buildings. To generate the map, RFMap first transmits RF signals in many different directions and then measures the distances of different reflectors inside the building. Next, it identifies these reflectors and classifies them into various types based on the properties of the reflections. A key challenge is that RFMap does not receive reflections from all the directions due to the specular nature of the reflectors. Due to this, it only gets sparse data about the objects in the environment. To address this challenge, RFMap trains a deep generative adversarial network (GAN) to intelligently predict the missing information. At runtime, it feeds the locations and types of the detected reflectors to the trained GAN and generates complete and accurate map. We implemented RFMap using software defined radios and extensively evaluated it in several real-world environments. Our results show that RFMap generated the maps of all the buildings that we tested it on with high accuracy.}, number={1}, journal={ACM TRANSACTIONS ON SENSOR NETWORKS}, author={Khan, Usman Mahmood and Venkatnarayan, Raghav H. and Shahzad, Muhammd}, year={2023}, month={Feb} }
 @article{khan_rigazio_shahzad_2022, title={Contactless Monitoring of PPG Using Radar}, volume={6}, ISSN={["2474-9567"]}, DOI={10.1145/3550330}, abstractNote={In this paper, we propose VitaNet, a radio frequency based contactless approach that accurately estimates the PPG signal using radar for stationary participants. The main insight behind VitaNet is that the changes in the blood volume that manifest in the PPG waveform are correlated to the physical movements of the heart, which the radar can capture. To estimate the PPG waveform, VitaNet uses a self-attention architecture to identify the most informative reflections in an unsupervised manner, and then uses an encoder decoder network to transform the radar phase profile to the PPG sequence. We have trained and extensively evaluated VitaNet on a large dataset obtained from 25 participants over 179 full nights. Our evaluations show that VitaNet accurately estimates the PPG waveform and its derivatives with high accuracy, significantly improves the heart rate and heart rate variability estimates from the prior works, and also accurately estimates several useful PPG features. We have released the codes of VitaNet as well as the trained models and the dataset used in this paper.}, number={3}, journal={PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT}, author={Khan, Usman Mahmood and Rigazio, Luca and Shahzad, Muhammad}, year={2022}, month={Sep} }
 @article{khan_shahzad_2022, title={Estimating Soil Moisture using RF Signals}, DOI={10.1145/3495243.3517025}, abstractNote={In this paper, we propose CoMEt, a radio frequency based approach that measures soil moisture at multiple depths underneath the ground surface without installing any objects in the soil and without making any contact with the ground surface. The main insight behind CoMEt is that the phase of an RF signal depends on its wavelength in the medium through which it is propagating, which in turn depends on the amount of soil moisture. To measure soil moisture, CoMEt leverages the phase changes across successive antennas in a receive antenna array along with the time of flight of the received signal to jointly estimate the depth of each layer of soil and the wavelength of the signal in each layer. It then uses these estimates to obtain the amount of moisture in each soil layer. We have implemented CoMEt using a software defined radio and a Raspberry Pi to measure soil moisture in real-time. We have extensively evaluated CoMEt in both indoor and outdoor environments. Our results show that CoMEt estimated soil moisture for up to three layers of soil with a median error of just 1.1%.}, journal={PROCEEDINGS OF THE 2022 THE 28TH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND NETWORKING, ACM MOBICOM 2022}, author={Khan, Usman Mahmood and Shahzad, Muhammad}, year={2022}, pages={242–254} }
 @article{iqbal_khan_khan_shahzad_2022, title={Left or Right: A Peek into the Political Biases in Email Spam Filtering Algorithms During US Election 2020}, DOI={10.1145/3485447.3512121}, abstractNote={Email services use spam filtering algorithms (SFAs) to filter emails that are unwanted by the user. However, at times, the emails perceived by an SFA as unwanted may be important to the user. Such incorrect decisions can have significant implications if SFAs treat emails of user interest as spam on a large scale. This is particularly important during national elections. To study whether the SFAs of popular email services have any biases in treating the campaign emails, we conducted a large-scale study of the campaign emails of the US elections 2020 by subscribing to a large number of Presidential, Senate, and House candidates using over a hundred email accounts on Gmail, Outlook, and Yahoo. We analyzed the biases in the SFAs towards the left and the right candidates and further studied the impact of the interactions (such as reading or marking emails as spam) of email recipients on these biases. We observed that the SFAs of different email services indeed exhibit biases towards different political affiliations.}, journal={PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22)}, author={Iqbal, Hassan and Khan, Usman Mahmood and Khan, Hassan Ali and Shahzad, Muhammad}, year={2022}, pages={2491–2500} }
 @article{khan_venkatnarayan_shahzad_2020, title={RFMap: Generating Indoor Maps using RF Signals}, DOI={10.1109/IPSN48710.2020.00-40}, abstractNote={Generating maps of indoor environments beyond the line of sight finds applications in several areas such as planning, navigation, and security. While researchers have previously explored the use of RF signals to generate maps, prior work has two important limitations: (i) it requires moving the mapping setup along the entire lengths of the sides of the building, and (ii) it generates maps that are not fully connected, rather are scatter plots of locations from where some obstacles reflected the signals. Thus, prior approaches require human interpretation to locate the walls and determine how they merge. In this paper, we address these limitations and propose RFMap, which generates fully connected maps, and does not require the measurement setup to be moved along the sides of the buildings. To generate the map, RFMap first transmits RF signals in many different directions by rotating the antennas while keeping them at the same location and then measures the distances of different reflectors inside the building. Next, it identifies these reflectors and classifies them into various types based on the properties of the reflections. A key challenge is that RFMap does not receive reflections from all the directions due to the specular nature of the reflectors. Due to this, it only gets sparse data about the objects in the environment. To address this challenge, RFMap trains deep generative adversarial network (GAN) to intelligently predict the missing information. At runtime, it feeds the locations and types of the detected reflectors to the trained GAN and generates the complete and accurate map. We implemented RFMap using software defined radios and extensively evaluated it in several real world environments. Our results show that RFMap generated the maps of all the buildings that we tested it on with high accuracy.}, journal={2020 19TH ACM/IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN 2020)}, author={Khan, Usman Mahmood and Venkatnarayan, Raghav H. and Shahzad, Muhammad}, year={2020}, pages={133–144} }
 @article{huppenkothen_bachetti_stevens_migliari_balm_hammad_khan_mishra_rashid_sharma_et al._2019, title={Stingray: A Modern Python Library for Spectral Timing}, volume={881}, ISSN={["1538-4357"]}, DOI={10.3847/1538-4357/ab258d}, abstractNote={Abstract}, number={1}, journal={ASTROPHYSICAL JOURNAL}, author={Huppenkothen, Daniela and Bachetti, Matteo and Stevens, Abigail L. and Migliari, Simone and Balm, Paul and Hammad, Omar and Khan, Usman Mahmood and Mishra, Himanshu and Rashid, Haroon and Sharma, Swapnil and et al.}, year={2019}, month={Aug} }