Efficient calculation of sensor utility and sensor removal in wireless sensor networks for adaptive signal estimation and beamforming

Wireless sensor networks are often deployed over a large area of interest and therefore the quality of the sensor signals may vary significantly across the different sensors. In this case, it is useful to have a measure for the importance or the so-called "utility" of each sensor, e.g., for sensor subset selection, resource allocation or topology selection. In this paper, we consider the efficient calculation of sensor utility measures for four different signal estimation or beamforming algorithms in an adaptive context. We use the definition of sensor utility as the increase in cost (e.g., mean-squared error) when the sensor is removed from the estimation procedure. Since each possible sensor removal corresponds to a new estimation problem (involving less sensors), calculating the sensor utilities would require a continuous updating of different signal estimators (where is the number of sensors), increasing computational complexity and memory usage by a factor. However, we derive formulas to efficiently calculate all sensor utilities with hardly any increase in memory usage and computational complexity compared to the signal estimation algorithm already in place. When applied in adaptive signal estimation algorithms, this allows for on-line tracking of all the sensor utilities at almost no additional cost. Furthermore, we derive efficient formulas for sensor removal, i.e., for updating the signal estimator coefficients when a sensor is removed, e.g., due to a failure in the wireless link or when its utility is too low. We provide a complexity evaluation of the derived formulas, and demonstrate the significant reduction in computational complexity compared to straightforward implementations

Utility based cross-layer collaboration for speech enhancement in wireless acoustic sensor networks

A wireless acoustic sensor network is considered that is used to estimate a desired speech signal that has been corrupted by noise. The application layer of the WASN derives an optimal filter in a linear MMSE sense. A utility function is then used in conjunction with the MMSE estimate in order to evaluate the most significant signal components from each node in the system. The utility values are used as a cross-layer link between the application layer and the network layer so the nodes transmit the signal components that are deemed most relevant to the estimate while adhering to the power constraints of the system. The simulation results show that a high signal-to-error and signal-to-noise ratio is still achievable while transmitting a subset of signal components

Stereophonic noise reduction using a combined sliding subspace projection and adaptive signal enhancement

A novel stereophonic noise reduction method is proposed. This method is based upon a combination of a subspace approach realized in a sliding window operation and two-channel adaptive signal enhancing. The signal obtained from the signal subspace is used as the input signal to the adaptive signal enhancer for each channel, instead of noise, as in the ordinary adaptive noise canceling scheme. Simulation results based upon real stereophonic speech contaminated by noise components show that the proposed method gives improved enhancement quality in terms of both segmental gain and cepstral distance performance indices in comparison with conventional nonlinear spectral subtraction approaches

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Packet based Inference and Control

Communication constraints in Networked Control systems are frequently limits on data packet rates. To efficiently use the available packet rate budgets, we have to resort to event-triggered packet transmission. We have to sample signal waveforms and transmit packets not at deterministic times but at random times adapted to the signals measured. This thesis poses and solves some basic design problems we face in reaching for the extra efficiency. We start with an estimation problem involving a single sensor. A sensor makes observations of a diffusion process, the state signal, and has to transmit samples of this process to a supervisor which maintains an estimate of the state. The objective of the sensor is to transmit samples strategically to the supervisor to minimize the distortion of the supervisor's estimate while respecting sampling rate constraints. We solve this problem over both finite and infinite horizons when the state is a scalar linear system. We describe the relative performances of the optimal sampling scheme, the best deterministic scheme and of the suboptimal but simple to implement level-triggered sampling scheme. Apart from the utility of finding the optimal sampling strategies and their performances, we also learnt of some interesting properties of the level-triggered sampling scheme. The control problem is harder to solve for the same setting with a single sensor. In the estimation problem for the linear state signal, the estimation error is also a linear diffusion and is reset to zero at sampling times. In the control problem, there is no equivalent to the error signal. We pay attention to an infinite horizon average cost problem where, the sampling strategy is chosen to be level-triggered. We design piece-wise constant controls by translating the problem to one for discrete-time Markov chain formed by the sampled state. Using results on the average cost control of Markov chains, we are able to derive optimality equations and iteratively compute solutions. The last chapter tackles a binary sequential hypothesis testing problem with two sensors. The special feature of the problem is the ability of each sensor to hear the transmissions of the other towards the supervisor. Each sensor is afforded on transmission of a sample of its likelihood ratio process. We restrict attention to level-triggered sampling. Although we are unable to demonstrate overall optimality of the asynchronous scheme we pursue, we are able to describe its advantages over other level-triggered schemes and of course the deterministic one. The chief merits of this thesis are the formulation and solution of some basic problems in multi-agent estimation and control. In the problems we have attacked, we have been able to deal with the differences in information patterns at sensors and supervisors. The main demerits are the ignoring of packet losses and of variable delays in packet transmissions. The situation of packet losses can however be handled at the expense of additional computations. To summarize, this thesis provides valuable generalizations of the works of Astrom and Bernhardsson and of Kushner on timing of Control actions and of Sampling observations respectively