Majid Emami

Application of time-reversal with MMSE equalizer to UWB communications

We propose to apply a technique called time-reversal to UWB communications. In time-reversal a signal is precoded such that it focuses both in time and in space at a particular receiver. Spatial focusing reduces interference to other co-existing systems. Due to temporal focusing, the received power is concentrated within a few taps and the task of equalizer design becomes much simpler than without focusing. Furthermore, temporal focusing allows a large increase in transmission rate compared to schemes that let the impulse response ring out before the next symbol is sent. Our paper introduces time-reversal, investigates the benefit of temporal focusing, and examines the performance of an MMSE-TR equalizer in an UWB channel.

Matched filtering with rate back-off for low complexity communications in very large delay spread channels

We study the possibility to transmit data over channels with large delay spreads under the constraint of a very simple receiver which has only one tap. Such a scheme is of interest when a cost-efficient way to transmit potentially high data rates are sought after. We investigate the performance of the optimal prefilter for this scheme and compare it to a simplified, so-called time-reversal (TR) prefilter which has very low complexity. At low SNRs, the TR prefilter and the optimal prefilter are equivalent. At high SNRs, the TR prefilter achieves a performance that is independent from the delay spread of the channel and hence its performance is the same for any bandwidth. In applications where bandwidth is abundant, such as ultra-wide band (UWB) communications, any required performance can be obtained by TR prefilters with a rate back-off transmission (i.e. transmission rate lower than the allowable bandwidth). Similar performance can also be obtained with full-rate transmission using several transmit antennas. This performance is guaranteed, since the high diversity of the large delay spread channel effectively eliminates any fading.

The automatic measurement of facial beauty

We develop an automatic facial beauty scoring system based on ratios between facial features. After isolating the face, eyes, eyebrows and mouth in a portrait photograph, we represent a face abstractly as an 8-element vector of ratios between these features. We use a variant of the K-nearest neighbor algorithm, in the context of a parameterized metric space optimized using a genetic algorithm, to learn a beauty assignment function from a training set of photographs rated by humans. We assess performance on a test set of photographs, concluding that when facial ratios are accurately extracted in the computer vision phase, the results of the program are highly correlated with median-human ratings of beauty.

Driver drowsiness detection using multimodal sensor fusion

This paper proposes a multi-modal sensor fusion algorithm for the estimation of driver drowsiness. Driver sleepiness is believed to be responsible for more than 30% of passenger car accidents and for 4% of all accident fatalities. In commercial vehicles, drowsiness is blamed for 58% of single truck accidents and 31% of commercial truck driver fatalities. This work proposes an innovative automatic sleep-onset detection system. Using multiple sensors, the driver’s body is studied as a mechanical structure of springs and dampeners. The sleep-detection system consists of highly sensitive triple-axial accelerometers to monitor the driver’s upper body in 3-D. The subject is modeled as a linear time-variant (LTV) system. An LMS adaptive filter estimation algorithm generates the transfer function (i.e. weight coefficients) for this LTV system. Separate coefficients are generated for the awake and asleep states of the subject. These coefficients are then used to train a neural network. Once trained, the neural network classifies the condition of the driver as either awake or asleep. The system has been tested on a total of 8 subjects. The tests were conducted on sleep-deprived individuals for the sleep state and on fully awake individuals for the awake state. When trained and tested on the same subject, the system detected sleep and awake states of the driver with a success rate of 95%. When the system was trained on three subjects and then retested on a fourth “unseen” subject, the classification rate dropped to 90%. Furthermore, it was attempted to correlate driver posture and sleepiness by observing how car vibrations propagate through a person’s body. Eight additional subjects were studied for this purpose. The results obtained in this experiment proved inconclusive which was attributed to significant differences in the individual habitual postures.

Estimation-pruning (EP) algorithm for point-to-point travel cost minimization in a non-FIFO dynamic network

This paper presents the estimation-pruning (EP) algorithm for finding the best path (with minimum cost) from a source to a destination in a dynamic network that does not necessarily obey the first-in-first-out (FIFO) property. The EP algorithm consists of two steps. The first step is the forward or the estimation step in which a bound on the traveling cost of each possible path is calculated. The second step is the backward or the pruning step in which the paths that are unlikely to produce the best route are eliminated. The resulting network is then expanded in time and is converted to a static network, which is used to find the best route.