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14
Sum Capacity of a Gaussian Vector Broadcast Channel
 IEEE Trans. Inform. Theory
, 2002
"... This paper characterizes the sum capacity of a class of nondegraded Gaussian vectB broadcast channels where a singletransmitter with multiple transmit terminals sends independent information to multiple receivers. Coordinat+[ is allowed among the transmit teminals, but not among the different recei ..."
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Cited by 193 (21 self)
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This paper characterizes the sum capacity of a class of nondegraded Gaussian vectB broadcast channels where a singletransmitter with multiple transmit terminals sends independent information to multiple receivers. Coordinat+[ is allowed among the transmit teminals, but not among the different receivers. The sum capacity is shown t be a saddlepoint of a Gaussian mu al informat]R game, where a signal player chooses a tansmit covariance matrix to maximize the mutual information, and a noise player chooses a fictitious noise correlation to minimize the mutual information. This result holds fort he class of Gaussian channels whose saddlepoint satisfies a full rank condition. Furt her,t he sum capacity is achieved using a precoding method for Gaussian channels with additive side information noncausally known at the transmitter. The optimal precoding structure is shown t correspond to a decisionfeedback equalizer that decomposes t e broadcast channel into a series of singleuser channels with intk ference presubtract] at the transmiter.
Pairwise Markov chains
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2003
"... Abstract. The restoration of a hidden process X from an observed process Y is often performed in the framework of hidden Markov chains (HMC). HMC have been recently generalized to triplet Markov chains (TMC). In the TMC model one introduces a third random chain U and assumes that the triplet T = (X, ..."
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Cited by 48 (25 self)
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Abstract. The restoration of a hidden process X from an observed process Y is often performed in the framework of hidden Markov chains (HMC). HMC have been recently generalized to triplet Markov chains (TMC). In the TMC model one introduces a third random chain U and assumes that the triplet T = (X, U, Y) is a Markov chain (MC). TMC generalize HMC but still enable the development of efficient Bayesian algorithms for restoring X from Y. This paper lists some recent results concerning TMC; in particular, we recall how TMC can be used to model hidden semiMarkov Chains or deal with nonstationary HMC.
Mapping partially observable features from multiple uncertain vantage points
 The International Journal of Robotics Research
, 2002
"... In this paper we present a technique for mapping partially observable features from multiple uncertain vantage points. The problem of concurrent mapping and localization (CML) is stated as follows. Starting from an initial known position, a mobile robot travels through a sequence of positions, obtai ..."
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Cited by 39 (9 self)
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In this paper we present a technique for mapping partially observable features from multiple uncertain vantage points. The problem of concurrent mapping and localization (CML) is stated as follows. Starting from an initial known position, a mobile robot travels through a sequence of positions, obtaining a set of sensor measurements at each position. The goal is to process the sensor data to produce an estimate of the trajectory of the robot while concurrently building a map of the environment. In this paper, we describe a generalized framework for CML that incorporates temporal as well as spatial correlations. The representation is expanded to incorporate past vehicle positions in the state vector. Estimates of the correlations between current and previous vehicle states are explicitly maintained. This enables the consistent initialization of map features using data from multiple time steps. Updates to the map and the vehicle trajectory can also be performed in batches of data acquired from multiple vantage points. The method is illustrated with sonar data from a testing tank and via experiments with a B21 land mobile robot, demonstrating the ability to perform CML with sparse and ambiguous data. KEY WORDS—mapping, navigation, mobile robots 1.
Algorithms and Representations for Reinforcement Learning
, 2005
"... “If we knew what it was we were doing, it would not be called research, would it?” ..."
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Cited by 37 (7 self)
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“If we knew what it was we were doing, it would not be called research, would it?”
An EMBased ForwardBackward Kalman Filter for the Estimation of TimeVariant Channels in OFDM
"... Abstract — OFDM modulation combines the advantages of high achievable rates and relatively easy implementation. However, for proper recovery of the input, the OFDM receiver needs accurate channel information. In this paper, we propose an expectationmaximization (EM) algorithm for joint channel and ..."
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Cited by 5 (2 self)
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Abstract — OFDM modulation combines the advantages of high achievable rates and relatively easy implementation. However, for proper recovery of the input, the OFDM receiver needs accurate channel information. In this paper, we propose an expectationmaximization (EM) algorithm for joint channel and data recovery in fast fading environments. The algorithm makes a collective use of the data and channel constraints inherent in the communication problem. This comes in contrast to other works which have employed these constraints selectively. The data constraints include pilots, the cyclic prefix, and the finite alphabet restriction, while the channel constraints include sparsity, finite delay spread, and the statistical properties of the channel (frequency and time correlation). The algorithm boils down to a forwardbackward (FB) Kalman filter. We also suggest a suboptimal modification that is able to track the channel and recover the data with no latency. Simulations show the favorable behavior of both algorithms compared to other channel estimation techniques. Index Terms — OFDM, timevariant channels, channel modelling, frequency correlation, time correlation, channel estimation, Kalman filters,
Bayesian smoothing algorithms in pairwise and triplet Markov chains
 in Proceedings of the 2005 IEEE Workshop on Statistical Signal Processing (SSP 05
, 2005
"... An important problem in signal processing consists in estimating an unobservable process x = {xn}n∈IN from an observed process y = {yn}n∈IN. In Linear Gaussian Hidden Markov Chains (LGHMC), recursive solutions are given by Kalmanlike Bayesian restoration algorithms. In this paper, we consider the m ..."
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Cited by 3 (3 self)
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An important problem in signal processing consists in estimating an unobservable process x = {xn}n∈IN from an observed process y = {yn}n∈IN. In Linear Gaussian Hidden Markov Chains (LGHMC), recursive solutions are given by Kalmanlike Bayesian restoration algorithms. In this paper, we consider the more general framework of Linear Gaussian Triplet Markov Chains (LGTMC), i.e. of models in which the triplet (x, r, y) (where r = {rn}n∈IN is some additional process) is Markovian and Gaussian. We address fixedinterval smoothing algorithms, and we extend to LGTMC the RTS algorithm by Rauch, Tung and Striebel, as well as the TwoFilter algorithm by Mayne and Fraser and Potter. 1.
Cmos Image Sensors Dynamic Range and SNR Enhancement via Statistical Signal Processing
"... Most of today's video and digital cameras use CCD image sensors, where the electric charge collected by the photodetector array during exposure time is serially shifted out of the sensor chip resulting in slow readout speed and high power consumption. Recently developed CMOS image sensors, by compar ..."
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Cited by 1 (1 self)
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Most of today's video and digital cameras use CCD image sensors, where the electric charge collected by the photodetector array during exposure time is serially shifted out of the sensor chip resulting in slow readout speed and high power consumption. Recently developed CMOS image sensors, by comparison, are read out nondestructively and in a manner similar to a digital memory and can thus be operated at very high frame rates. A CMOS image sensor can also be integrated with other camera functions on the same chip ultimately leading to a singlechip digital camera with very compact size, low power consumption and additional functionality. CMOS image sensors, however, generally su#er from lower dynamic range than CCDs due to their high read noise and nonuniformity. Moreover, as sensor design follows CMOS technology scaling, well capacity will continue to decrease, eventually resulting in unacceptably low SNR.
WRITING ON DIRTY PAPER WITH FEEDBACK ∗
"... Abstract. “Writing on dirty paper ” refers to the communication problem over a channel with both noise and interference, where the interference is known to the encoder noncausally and unknown to the decoder. This problem is regarded as a basic building block in both the singleuser and multiuser co ..."
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Cited by 1 (1 self)
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Abstract. “Writing on dirty paper ” refers to the communication problem over a channel with both noise and interference, where the interference is known to the encoder noncausally and unknown to the decoder. This problem is regarded as a basic building block in both the singleuser and multiuser communications, and it has been extensively investigated by Costa and other researchers. However, little is known in the case that the encoder can have access to feedback from the decoder. In this paper, we study the dirtypaper coding problem for feedback Gaussian channels without or with memory. We provide the most power efficient coding schemes for this problem, i.e., the schemes achieve lossless interference cancelation. These schemes are based on the Kalman filtering algorithm, extend the SchalkwijkKailath feedback codes, have low complexity and a doubly exponential reliability function, and reveal the interconnections among information, control, and estimation over dirtypaper channels with feedback. This research may be found useful to, for example, powerconstrained sensor network communication. Key words: Feedback communication; Dirtypaper coding; Lossless interference cancelation; Capacityachieving coding scheme; Interconnections among information, control, and estimation
Linear minimum variance estimation fusion
, 2004
"... This paper shows that a general multisensor unbiased linearly weighted estimation fusion essentially is the linear minimum variance (LMV) estimation with linear equality constraint, and the general estimation fusion formula is developed by extending the GaussMarkov estimation to the random paramete ..."
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This paper shows that a general multisensor unbiased linearly weighted estimation fusion essentially is the linear minimum variance (LMV) estimation with linear equality constraint, and the general estimation fusion formula is developed by extending the GaussMarkov estimation to the random parameter under estimation. First, we formulate the problem of distributed estimation fusion in the LMV setting. In this setting, the fused estimator is a weighted sum of local estimates with a matrix weight. We show that the set of weights is optimal if and only if it is a solution of a matrix quadratic optimization problem subject to a convex linear equality constraint. Second, we present a unique solution to the above optimization problem, which depends only on the covariance matrix k . Third, if a priori information, the expectation and covariance, of the estimated quantity is unknown, a necessary and sufficient condition for the above LMV fusion becoming the best unbiased LMV estimation with known prior information as the above is presented. We also discuss the generality and usefulness of the LMV fusion formulas developed. Finally, we provide an offline recursion of k for a class of multisensor linear systems with coupled measurement noises.
Control and Cybernetics
"... Robust impedance control of a piezoelectric stage under thermal and external load disturbances ∗ by ..."
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Robust impedance control of a piezoelectric stage under thermal and external load disturbances ∗ by