About

Dr. Masoud Salehi is an Associate Professor and Associate Chair for Graduate Studies in the Department of Electrical and Computer Engineering at Northeastern University College of Engineering. He received his BS degree with Summa Cum Laude from Tehran University and his MS and Ph.D. degrees from Stanford University, all in Electrical Engineering. Prior to joining Northeastern in 1989, he was affiliated with the Department of Electrical and Computer Engineering at Isfahan University of Technology and the Department of Electrical Engineering at Tehran University. He also served as a visiting professor at Eindhoven University of Technology in the Netherlands, where he conducted research in network information theory and coding of storage media. His main areas of research interest include network information theory, source-channel matching problems in single and multiple user systems, data compression, turbo coding, coding for fading channels, and digital watermarking. Dr. Salehi's research has been supported by various organizations including the National Science Foundation, GTE, NUWC, CenSSIS, and Analog Devices. He has also provided industry consulting to companies such as Teleco Oilfield Services and AT&T. Additionally, he is a member of the Editorial Board of The International Journal of Electronics and Communications. Dr. Salehi is a coauthor of several textbooks on communication systems, which have been translated into multiple languages.

Research topics

• Computer Science
• Artificial Intelligence
• Computer vision
• Computer graphics (images)
• Human–computer interaction
• Geology
• Art
• Computer Security
• Aesthetics
• Mathematics
• Paleontology
• Computer network
• Telecommunications

Selected publications

• Cybersickness Detection through Head Movement Patterns: A Promising Approach

  arXiv (Cornell University) · 2024 · 7 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Human–computer interaction

  Despite the widespread adoption of Virtual Reality (VR) technology, cybersickness remains a barrier for some users. This research investigates head movement patterns as a novel physiological marker for cybersickness detection. Unlike traditional markers, head movements provide a continuous, non-invasive measure that can be easily captured through the sensors embedded in all commercial VR headsets. We used a publicly available dataset from a VR experiment involving 75 participants and analyzed head movements across six axes. An extensive feature extraction process was then performed on the head movement dataset and its derivatives, including velocity, acceleration, and jerk. Three categories of features were extracted, encompassing statistical, temporal, and spectral features. Subsequently, we employed the Recursive Feature Elimination method to select the most important and effective features. In a series of experiments, we trained a variety of machine learning algorithms. The results demonstrate a 76% accuracy and 83% precision in predicting cybersickness in the subjects based on the head movements. This study contribution to the cybersickness literature lies in offering a preliminary analysis of a new source of data and providing insight into the relationship of head movements and cybersickness.

  PublisherOA PDFDOI

• Cybersickness Detection Through Head Movement Patterns: A Promising Approach

  Lecture notes in computer science · 2024 · 16 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science
  PublisherDOI

• Physical Layer Security in Multi-User Wireless Networks: Interference Effect on Large Scale Analysis

  2020 · 1 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Computer network

  We study the secrecy sum-rate (SSR) of a multi-user system in the presence of artificial noise (AN) and determine conditions under which it is necessary to add AN assuming eavesdropper (Eve) cannot remove the interference. We first derive a closed-form expression for the asymptotic SSR of the system and determine the optimum power allocation. The closed form expressions for the SSR and optimum power allocation closely match our simulation results. We also show that as the number of receive antennas at Eve increases, we need to allocate more power to AN in order to degrade reception at Eve.

  PublisherDOI

• Multi-Carrier Jamming Mitigation: A Proactive Game Theoretic Approach

  Advances in information security · 2019-01-01

  book-chapterSenior author
  PublisherDOI

• Large-Scale Analysis of Regularized Block Diagonalization Precoding for Physical Layer Security of Multi-User MIMO Wireless Networks

  IEEE Transactions on Vehicular Technology · 2019-04-27 · 13 citations

  articleSenior author

  In this paper, we propose regularized block diagonalization (RBD) precoding using artificial noise (AN) for physical layer security in downlink multi-user MIMO wireless networks. We derive secrecy sum rate and asymptotic secrecy sum rate for the proposed scheme. The optimum power allocation for legitimate users and the AN signal are derived in closed form for optimal asymptotic secrecy sum rate. Our analysis shows that to achieve best performance, it is more efficient to degrade the performance of the eavesdropper than improving legitimate users' rates. We also study the impact of error in channel estimation on the system and derive closed-form SINR and secrecy sum-rate expressions for this case. Simulations show that the secrecy sum rate of RBD precoding outperforms the secrecy sum rate of regularized channel inversion by 0.5 bits/s/Hz. We also show that in the presence of channel estimation error, our approximate closed forms for secrecy sum rate are very closed to the actual secrecy sum rate over a wide range of SNR and channel estimation error values.

  PublisherDOI

• Large-Scale Analysis of Physical-Layer Security in Multi-User Wireless Networks

  IEEE Transactions on Communications · 2018-08-30 · 8 citations

  article

  We derive the asymptotic ergodic secrecy sum rate (ESSR) of block diagonalization precoding for physical-layer security in downlink multi-user wireless networks that use artificial noise. First, we derive the ESSR of the system. Then, we derive the power allocation and the asymptotic ESSR as the number of antennas increases. After, we derive an approximate expression for the optimum ratio of the number of transmit antennas to the number of receive antennas (Ψ). We then introduce the user dropping factor (UDF) as a means to derive the optimum number of users in the system under different scenarios and then derive the asymptotic optimum UDF, which gives the optimum number of scheduled users. We also study the effect of imperfect channel state information and derive a closed-form ESSR. Simulation results show that these precoders outperform channel inversion (CI) and regularized CI schemes in terms of the ESSR at all SNRs. We also show that as the number of receive antennas of each user increases, the asymptotic ESSR gap also increases.

  PublisherDOI

• Large scale analysis of physical layer security in multi-user wireless networks

  2017-05-01 · 6 citations

  article

  We derive the asymptotic secrecy sum-rate of block diagonalization (BD) precoding for physical layer security in downlink multi-user wireless networks using artificial noise (AN). We first derive the secrecy sum-rate assuming that the channel of the eavesdropper is not known at the transmitter and then the asymptotic secrecy sum-rate is derived. The optimum power allocation for asymptotic secrecy sum-rate is derived after that. Simulation results show that the proposed precoders outperform channel inversion (CI) and regularized channel inversion (RCI) schemes in terms of the secrecy sum-rate at all SNRs. We also show that as the number of receive antennas of each user increases, the sum-rate gap between the proposed scheme and existing methods increases.

  PublisherDOI

• Large Scale Analysis of RBD Precoding for Physical Layer Security in Multi-User Wireless Networks

  2017-12-01 · 3 citations

  articleSenior author

  We analyze asymptotic performance of regularized block diagonalization (RBD) precoding along with artificial noise (AN) used for physical layer security in downlink multi-user wireless networks. We derive the secrecy and asymptotic secrecy sum-rates for this scheme. We also derive closed form expressions for the asymptotic power allocation to the AN signal and legitimate users. Simulations show RBD precoding outperform regularized channel inversion (RCI) in terms of secrecy sum-rate with a margin of 0.5 bits/s/Hz.

  PublisherDOI

• Neighborhood total domination of a graph and its complement.

  Australas. J Comb. · 2016-01-01 · 1 citations

  article
  Publisher

• Trapping sets in stochastic LDPC decoders

  2015-11-01 · 3 citations

  articleSenior author

  Stochastic decoding is a hardware and energy-efficient approach to implement iterative Low-Density Parity-Check (LDPC) decoders. However, stochastic decoding often has a performance loss compared to the sum-product algorithm and suffers from additional trapping sets, which limit the performance of LDPC codes in the error floor region. In this paper, we investigate the error characteristics of stochastic LDPC decoding. We also report stochastic-decoding-specific trapping sets for the (1056,528) LDPC code from the WiMAX standard. This observation has a potential to guide code structure designs that lower the error floor in stochastic decoding.

  PublisherDOI

Frequent coauthors

• Y. Wang

  Fresenius Medical Care North America (United States)

  18 shared

• Gero von Gersdorff

  University Hospital Cologne

  15 shared

• Tolga M. Duman

  Bilkent University

  14 shared

• Claudia Klein

  Département Santé Animale

  12 shared

• C. Santimbrean

  12 shared

• C. Barbulescu

  12 shared

• J.G. Proakis

  12 shared

• Gabriel Mircescu

  Carol Davila University of Medicine and Pharmacy

  10 shared

Awards & honors

• 2024 Outstanding Faculty Service Award