A significant challenge of adopting battery electric buses into fleets lies in scheduling the charging, which in turn is complicated by considerations such as timing constraints imposed by routes, long charging times, limited numbers of chargers, and utility cost structures. This work builds on previous network-flow-based charge scheduling approaches and includes both consumption and demand time-of-use costs while accounting for uncontrolled loads on the same meter. Additionally, a variable-rate, non-linear partial charging model compatible with the mixed-integer linear program (MILP) is developed for increased charging fidelity. To respond to feedback in an uncertain environment, the resulting MILP is adapted to a hierarchical receding horizon planner that utilizes a static plan for the day as a reference to follow while reacting to stochasticity on a regular basis. This receding horizon planner is analyzed with Monte-Carlo techniques alongside two other possible planning methods. It is found to provide up to 52% cost savings compared to a non-time-of-use aware method and significant robustness benefits compared to an optimal open-loop method.

As transit authorities begin adopting Battery Electric Buses (BEBs), they must address the challenge of extended battery charging sessions while maintaining fixed bus schedules and managing the cost of energy. This paper proposes a novel technique for minimizing the monthly cost of energy for operating electric bus fleets in the presence of time-varying uncontrolled loads. The charging problem is cast as a constrained bin packing problem and expressed as a mixed integer linear program (MILP). Among other things, we show that the proposed method significantly decreases demand on power infrastructure by temporally balancing the loads from buses with uncontrolled loads.

A major challenge to adopting battery electric buses into bus fleets is the scheduling of the battery charging while considering route timing constraints and battery charge. This work develops a scheduling framework to balance the use of slow and fast chargers assuming the bus routes and charger locations are fixed. Slow chargers are utilized when possible to lower the cost of charging and fast chargers are used when needed to meet timing constraints and to ensure a sufficient charge for route execution. A directed graph is used to model the available charge times for buses that periodically return to the station to pick up passengers and to recharge its battery. A constrained network flow Mixed-Integer Linear Program (MILP) problem is formulated to optimize the scheduling of chargers as well as to determine the number of chargers required to meet battery state of charge thresholds. Using a randomly generated route schedule for thirty buses, results are presented that demonstrate the ability of the proposed method to find optimal charging plans while considering time-of-use (TOU) costs and allowing for fixed and variable numbers of chargers. These optimal charging plans reduce costs up to 20.9% compared to a thresholding-based plan and up to 12.3% compared to an optimal strategy that does not consider the TOU costs.

Adaptation using contravariant, sometimes called the natural gradient, provides true steepest descent learning on a manifold such as the manifold of invertible matrices or the manifold of invertible matrix transfer functions. In this letter, the contravariant gradient on the manifold of invertible matrix transfer functions is presented using the language of differential geometry. The covariant gradient (the usual gradient) is converted to the contravariant or natural gradient using the inverse of a Riemannian metric tensor, which is derived for the manifold of invertible matrix transfer functions. This has not been previously done using the language of differential geometry.

This letter extends previous work on contravariant adaptation by providing a formula for the contravariant (natural) gradient on the manifold of multivariable, causal, invertible, finite impulse response (FIR) systems. The right action on the manifold of multivariable causal Toeplitz systems is defined. Using this right action, a bilinear form defined on the tangent space at the identity is extended throughout the entire manifold by invariance. This results in a formula analogous to the natural gradient for matrices, but which preserves the causal, FIR nature of the system. The contravariant conversion factor is block Toeplitz structured, so that implementations may employ fast Fourier transform based convolutions to produce lower complexity than would a comparably-sized generic matrix natural gradient.

Smart grid uses an advanced metering infrastructure to create a two-way communication network between smart grid components and machine-to-machine communications have a great potential to implement this communication network. In this paper, we propose a one-layer aggregation-based machine-to-machine architecture for advanced metering infrastructure architecture of smart grid and focus on finding the optimum number of aggregators in its neighborhood area network based on three metrics: energy consumption of each aggregator, relay and aggregators cost, and delay. We develop an energy consumption model for each aggregator. We also define delay and a new performance metric, the product of cost and energy (CE), so that by minimizing it, we can obtain the optimum number of aggregators. The simulation results indicate correctness of our theoretical model and show that we can achieve a balance between cost, delay, and energy.

This paper addresses the video processing problem of passively estimating the heart rate of a human subject that is being video recorded in ambient light in natural conditions in which some motion is allowable. This extends previous work in which control over lighting and motion are required. A mathematical model is developed for the measured pixels on the subject's forehead. This model includes (1) a Markov process that accounts for the large scale but slowly fluctuating variations in reflected light, and (2) a quasi-periodic process to model the relatively small heart beat component. A framework for estimating these components is proposed and results of applying this method to real data recordings is presented. Results are compared to heart rate obtained from a pulse oximeter attached to the subject's finger.

Over the past decade, underrepresentation of minority students in science, technology, engineering, and mathematics (STEM) at the undergraduate and graduate level have been reported. However, little is known about the internal and external factors/experiences causing students to decide to drop-out. This study considers the gateway experiences (B.S. to M.S.) that led six STEM college students to continue to pursue their master's degree. This paper will present a novel method to triangulate affective responses of participants as they recollect their college experiences. We will discuss the graduate student case studies by comparing gender. The information presented highlights interview summaries from the graduate students, physiological instrumentation data including: (1) wireless non-invasive wrist sensors to measure skin electrical conductance (electrodermal activity) as a trigger for emotional response, (2) wired pulse oximeters to non-invasively monitor an individual's heart rate, and (3) preliminary detection of heart rate using a custom-developed, non-invasive/non-contact video monitoring technology presented for the first time in this paper. Together, the findings present a unique method to assess emotional and decision-making responses of students. Through this work, we hope to develop future research strategies that are qualitatively and quantitatively based for the target population.

Simultaneously estimating position and velocity of moving targets using only phase information from single-channel SAR data is impossible. This paper defines classes of equivalent target motion and solves the GMTI problem up to membership in an equivalence class using single-channel SAR phase data. We present a definitions for endo- and exo-clutter that is consistent with the equivalence classes, and show that most target motion can be detected, i.e. the set of endo-clutter targets is very small. We exploit the sparsity of moving targets in the scene to develop an algorithm to resolve target motion up to membership in an equivalence class, and demonstrate the effectiveness of the proposed technique using simulated data.

Digital down-conversion in software defined radios in space-to-Earth and Earth-to-space communications is challenging due to potentially large Doppler frequency offsets. The predictability of satellite orbits suggests that a computed Doppler offset could be used to perform an orbit-informed down-conversion. However, publicly available orbital parameters and orbit propagator codes lack the accuracy required for fine Doppler removal. This paper proposes a technique to adjust the orbital parameters until the computed Doppler profile matches the Doppler profile in the received signal. This technique is applied to a real signal recorded from a satellite overpass and is shown to be able to very accurately estimate frequency offsets.

Speech recognition is a classic example of a human/machine interface, typifying many of the difficulties and opportunities of human/machine interaction. In this paper, speech recognition is used as an example of applying turbo processing principles to the general problem of human/machine interface. Speech recognizers frequently involve a model representing phonemic information at a local level, followed by a language model representing information at a nonlocal level. This structure is analogous to the local (e.g., equalizer) and nonlocal (e.g., error correction decoding) elements common in digital communications. Drawing from the analogy of turbo processing for digital communications, turbo speech processing iteratively feeds back the output of the language model to be used as prior probabilities for the phonemic model. This analogy is developed here, and the performance of this turbo model is characterized by using an artificial language model. Using turbo processing, the relative error rate improves significantly, especially in high-noise settings.

In some modeling scenarios, particularly those representing data from natural sources, the discrete states conventionally used in hidden Markov models (HMMs) are at best an approximation, since the discrete states are a modeling artifact. In this paper we present an HMM in which the states take any value in a simplex. The Dirichlet distribution is used to provide a parsimonious representation of the distribution of the states. Conditional state estimates using an extension of the conventional forward/backward method, using Dirichlet distributions to provide a nearly closed-form, but approximate, representation.

This paper addresses automatic people matching in the dynamic setting of public transportation, such as a bus, as people enter and then at some later time exit from a doorway. Matching a person entering to the same person exiting at a later time provides accurate information about individual riders, such as how long a person is on a bus and the associated stops the person uses. At a higher level, matching exits to previous entry events provides information about the distribution of traffic flow across the whole transportation system. The proposed techniques may be applied at any gateway where the flow of human traffic is to be analyzed. For the purpose of associating entry and exit events, a trellis optimization algorithm is used for sequence estimation, based on multiple texel camera measurements. Since the number of states in the trellis exponentially grows with the number of persons currently on the bus, a beam search pruning technique is employed to manage the computational and memory load. Experimental results using real texel camera measurements show 96% matching accuracy for 68 people exiting a bus in a randomized order. In a bus route simulation where a true traffic flow distribution is used to randomly draw entry and exit events for simulated riders, the proposed sequence estimation algorithm produces an estimated traffic flow distribution, which provides an excellent match to the true distribution.

Echo cancelers typically employ control mechanisms to prevent adaptive filter updates during double-talk events. By contrast, this paper exploits the information contained in time-varying second order statistics of nonstationary signals to update adaptive filters and learn echo path responses during double-talk. First, a framework is presented for describing mixing and blind separation of independent groups of signals. Then several echo cancellation problems are cast in this framework, including the problem of simultaneous acoustic and line echo cancellation as encountered in speaker phones. A maximum-likelihood approach is taken to estimate both the unknown signal statistics as well as echo canceling filters. When applied to speech signals, the techniques developed in this paper typically achieved between 30 and 40 dB of echo return loss enhancement (ERLE) during continuous double-talking.

This paper develops a receiver for a multiple access scenario obtained by simply overlaying transmitted signals from different users, with (essentially) the same carrier, same modulation format, etc., without the benefit of conventional multiple access signaling. A joint a posteriori probability detector is derived for co-received signals. The receiver structure follows the familiar general model for turbo processing, employing a soft-in-soft-out joint detector followed by error correction decoding, which feeds back soft information in the form of extrinsic probabilities. Since the receiver is intended to function with an existing transmission system with no modifications to the transmitter, channelization methods and/or interleavers which would normally be designed into a multi-access system are absent. The general structure is demonstrated using a system employing LDPC decoding and simulated performance results are presented.

Several authors have developed a Hadamard (variously called FFT) transform technique for fast belief propagation over$GF(q)$, where in all prior work$q=2^{m}$for some$m$. The belief propagation step which employs the transform generalizes Gallager's lemma for computing the distribution of a sum of variables over a finite field. In this paper, the limitation to fields of characteristic 2 is eliminated, so that computations can take place over finite fields of arbitrary prime characteristic. This opens the door, for example, for BP decoding algorithms over the same fields that are used for algebraic geometry codes.

A unique Research Experience for Undergraduates (REU) was offered at Utah State University in the summer of 2009, with research topics drawn from signal processing, digital communications, and error correction coding. Thirteen students participated in this ten-week program, working on nine separate projects. The program began with two weeks of intense instruction with five accompanying programming and writing assignments. Students were presented with a number of projects to choose from according to their preparation and preference, developing in them the independence and “buy in” necessary for effective original research. Regular social activities were also planned to develop the research team and keep energy up. Results include several conference and journal papers and contributions to other ongoing research projects.

An iterative least square method is presented for estimating the solution to the lidar equation. The method requires knowledge of the backscatter values at a boundary point for all channels and a priori defined relationships between backscatter, extinction, and mass-fraction concentration for all scattering components. The lidar equation is formulated in vector form, and a solution is computed using an iterative least square technique. The solution is stable for signals with extremely low signal-to-noise ratios and for signals at ranges far beyond the boundary point. The solution can be applied to lidar signals with an arbitrary number of wavelengths and scattering components.

This paper explores the use of kurtosis in synchronizing with a QPSK burst transmission. The start and end of the burst are a priori unknown and are detected without the use of pilot signals. After frame level synchronization, signal level synchronization is performed. The symbol timing, carrier frequency and carrier phase are recovered without the assistance of known symbols or synchronization words in the data stream, i.e. totally blind synchronization. The performance of frame, symbol and carrier synchronization algorithms is evaluated through computer simulations.

The eigenmessage approach to iterative decoding introduces a degree of nonlocality into a belief propagation decoder by representing an entire set of messages around a cycle of the Tanner graph as a linear operator. The eigenvector for the operator represents a fixed point of the belief propagation algorithm around a cycle, with incident messages fixed. A multiple eigenmessage approach is also presented, in which messages around several cycles are simultaneously expressed. The eigenmessage approach may be applied to any graph with cycles, but we demonstrate its use on LDPC decoding. In this setting, computational results compare eigenmessage methods with conventional belief propagation decoding showing, using simulation and EXIT charts, that the eigenmessage approaches slightly reduce the number of decoder iterations compared to belief propagation decoding while preserving the probability of error of conventional decoding.

The Sudoku puzzle is a discrete constraint satisfaction problem, as is the error correction decoding problem. We propose here an algorithm for solution to the Sinkhorn puzzle based on Sinkhorn balancing. Sinkhorn balancing is an algorithm for projecting a matrix onto the space of doubly stochastic matrices. The Sinkhorn balancing solver is capable of solving all but the most difficult puzzles. A proof of convergence is presented, with some information theoretic connections. A random generalization of the Sudoku puzzle is presented, for which the Sinkhorn-based solver is also very effective.

By using a formulation similar to a Sobolev gradient for the natural gradient a new algorithm has been developed that converges faster, uses fewer additional parameters and has smaller storage requirements all while not overtraining to a training set. Simulation results show the improvements for an applicable problem.

Graphical modeling software tools have become commonplace in educational laboratories associated with courses in signal processing and communication systems. This paper explains that the purely graphical approach to modeling and simulation can be improved upon by combining graphical modeling with some low-level programming tasks. To implement this philosophy, a new software tool called EduCOM was developed. EduCOM offers a graphical modeling environment to construct system models. It also translates a graphical model into C/C++ code that can be compiled into an executable to simulate the model. Students are required to program the functionality of important blocks in their models such as filters, minimum-distance hard-decision rules, and look-up-tables. Anecdotal evidence is provided indicating that this approach offers an improved learning experience over pure graphical modeling approaches.

Frequency-domain least-squares filter design is extended to the case that magnitude and phase are not specified for all test frequencies. Unspecified quantities are obtained by an iterative, alternating projection algorithm. Examples of gain-only and phase-only identification are provided, as well as filter design.

Subband-based system identification is considered. The analysis in this paper gives geometric insights into the source of error in subband-based system identification. In particular, the mean-squared error arises as the residual after an orthogonal projection onto a subspace defined by the analysis filter impulse response. The minimum mean-squared identification error, which depends on the impulse response of the unknown system, is shown to be upper bounded by the error in a deterministic least-squares problem that involves the analysis filter response but is independent of the unknown system. The upper bounds may be used as criteria for analysis filter design that minimize the mean-squared error for the worst case unknown system, i.e., this is a minimax approach. An alternative subband processing structure is inspired by minimizing the projection residual. Examples show that significant reduction in minimum mean-squared error is possible.

The well-known Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm was generalized to compute joint posterior probabilities of arbitrary sets of symbols given noisy observations of those symbols at the output of an intersymbol interference (ISI) channel. This letter explores using pair-wise joint posterior probabilities produced by generalized BCJR together with expectation maximization for blind identification of the ISI channel impulse response and noise variance. Simulations indicate that the blind algorithm accurately estimates the channel response and noise variance and yields bit error rates comparable to a channel-informed BCJR equalizer.

Natural gradient learning has been shown to avoid singularities in the parameter space of multilayer perceptrons. However, it requires a large number of additional parameters beyond ordinary backpropagation. The article describes a new approach to natural gradient learning in which the number of parameters necessary is much smaller than in the natural gradient algorithm. This new method exploits the algebraic structure of the parameter space to reduce the space and time complexity of the algorithm and improve its performance.

A generalization of the BCJR algorithm is derived to compute joint posterior probabilities of arbitrary sets of symbols given received data. A special case of the new algorithm that computes pairwise joint probabilities is used to evaluate the second order statistics needed for EM estimation of carrier phase and symbol timing offsets in a digitally modulated waveform. This EM estimator is integrated into a turbo synchronization loop in which soft information is exchanged between the generalized BCJR algorithm (the synchronizer) and an LDPC decoder.

We present two lemmas relating polynomials occurring in the derivations of the Welch-Berlekamp (WB) key equation for Reed-Solomon codes. The lemma provides the connection between the conventional Welch-Berlekamp form of the key equation and the nullspace-based derivation of Dabiri and Blake. The second lemma allows the error values for the Dabiri Blake formulation to be computed based on the error values for the Welch-Berlekamp form.

The paper develops an extension of the adaptive RLS-type algorithm proposed by X.-L. Zhu and X.-D. Zhang (see IEEE Sig. Process. Lett., vol.9, no.12, p.432-5, 2002). Their work uses the matrix inversion lemma to solve iteratively the equation obtained from the natural gradient of the nonlinear principle component analysis problem. We reduce the complexity of the solution by applying the Euclidean direction search concept in place of the matrix inversion lemma. The simulations performed show that the convergence rate is comparable, albeit slower, but with reduced complexity per iteration.

The paper develops a solution to the task of tracking the position and movement of large (relative to pixel and image size) bodies through a visible-light camera's field of view. Noise sources such as dynamic backgrounds, vibration, variation in appearance, and rapidly changing lighting environments contribute to the complexity. Given that a low number of targets may be present at any one time and some assumptions about the dynamic nature of the image and targets, a solution to this problem is formulated which localizes and tracks objects in the field of view. The algorithm is capable of distinguishing among multiple targets which are in close proximity to the camera and to each other. A major consideration in the development is that the implemented system should be able to process the data in real time with moderate computational power. The present area of application is that of automatically generating ridership statistics for transit agencies: to count persons getting on or off a bus.

The problem of simultaneously calculating the discrete Fourier transform (DFT) of a real N-point sequence and the inverse discrete Fourier transform (IDFT) of the DFT of a real N-point sequence using a single DFT is considered. New formulas are given that exploit the symmetries of the DFT.

This work considers the problem of jointly estimating symbol frame boundaries and carrier frequency offsets for orthogonal frequency division multiplexed (OFDM) communications in frequency selective fading environments. Orthogonality between the modulated and virtual carriers over an interference free window of the received signal is used to develop an algorithm for estimating the carrier frequency offset and detecting the beginning of a symbol frame. By using a cyclic prefix to remove interference from neighboring frames, the the method is applicable in the presence of dispersive channels. The main contribution of this work is the joint estimation of the frequency offset and the frame boundary.

The mathematical theory of kernel (null space) structure of Hankel and Hankel-like matrices is applied to the problem of blind equalization of co-channel signals. This work builds on recently introduced ideas in blind equalization where the symbols are treated as deterministic parameters and estimated directly without estimating the channel first. The main contribution of the new approach is that it allows the simultaneous exploitation of shift structure in the data model and the finite alphabet property of the signals.

This paper considers the problem of recovering an unknown signal transmitted over an unknown (but stationary) multipath channel, and received by a narrowband array with unknown calibration. Unlike previously proposed multichannel blind equalization techniques, the methods described herein employ a model based on physical channel parameters rather than unstructured multiple output FIR filters. The algorithms exploit the structure of the signal and noise subspaces of the array output data when transformed to the frequency domain. Two approaches are presented. The first is an ESPRIT-like solution that provides a closed-form, but suboptimal, blind signal estimate. The second is based on maximum likelihood and, though requiring a search, is easily initialized with the ESPRIT solution. A mathematical development of the two algorithms is given, and their advantages and disadvantages relative to other currently available techniques are discussed.