21 research outputs found
Distributed Detection over Fading MACs with Multiple Antennas at the Fusion Center
A distributed detection problem over fading Gaussian multiple-access channels
is considered. Sensors observe a phenomenon and transmit their observations to
a fusion center using the amplify and forward scheme. The fusion center has
multiple antennas with different channel models considered between the sensors
and the fusion center, and different cases of channel state information are
assumed at the sensors. The performance is evaluated in terms of the error
exponent for each of these cases, where the effect of multiple antennas at the
fusion center is studied. It is shown that for zero-mean channels between the
sensors and the fusion center when there is no channel information at the
sensors, arbitrarily large gains in the error exponent can be obtained with
sufficient increase in the number of antennas at the fusion center. In stark
contrast, when there is channel information at the sensors, the gain in error
exponent due to having multiple antennas at the fusion center is shown to be no
more than a factor of (8/pi) for Rayleigh fading channels between the sensors
and the fusion center, independent of the number of antennas at the fusion
center, or correlation among noise samples across sensors. Scaling laws for
such gains are also provided when both sensors and antennas are increased
simultaneously. Simple practical schemes and a numerical method using
semidefinite relaxation techniques are presented that utilize the limited
possible gains available. Simulations are used to establish the accuracy of the
results.Comment: 21 pages, 9 figures, submitted to the IEEE Transactions on Signal
Processin
Generalizability and Application of the Skin Reflectance Estimate Based on Dichromatic Separation (SREDS)
Face recognition (FR) systems have become widely used and readily available
in recent history. However, differential performance between certain
demographics has been identified within popular FR models. Skin tone
differences between demographics can be one of the factors contributing to the
differential performance observed in face recognition models. Skin tone metrics
provide an alternative to self-reported race labels when such labels are
lacking or completely not available e.g. large-scale face recognition datasets.
In this work, we provide a further analysis of the generalizability of the Skin
Reflectance Estimate based on Dichromatic Separation (SREDS) against other skin
tone metrics and provide a use case for substituting race labels for SREDS
scores in a privacy-preserving learning solution. Our findings suggest that
SREDS consistently creates a skin tone metric with lower variability within
each subject and SREDS values can be utilized as an alternative to the
self-reported race labels at minimal drop in performance. Finally, we provide a
publicly available and open-source implementation of SREDS to help the research
community. Available at https://github.com/JosephDrahos/SRED
OFDM systems for wireless communications
Orthogonal Frequency Division Multiplexing (OFDM) systems are widely used in the standards for digital audio/video broadcasting, WiFi and WiMax. Being a frequency-domain approach to communications, OFDM has important advantages in dealing with the frequency-selective nature of high data rate wireless communication channels. As the needs for operating with higher data rates become more pressing, OFDM systems have emerged as an effective physical-layer solution.This short monograph is intended as a tutorial which highlights the deleterious aspects of the wireless channel and presents why OFDM i