Anthony Kuh

Information capacity of associative memories

Associative memory networks consisting of highly interconnected binary-valued cells have been used to model neural networks. Tight asymptotic bounds have been found for the information capacity of these networks. The authors derive the asymptotic information capacity of these networks using results from normal approximation theory and theorems about exchangeable random variables. >

Signal Processing Techniques for Knowledge Extraction and Information Fusion

This book brings together the latest research achievements from various areas of signal processing and related disciplines in order to consolidate the existing and proposed new directions in DSP based knowledge extraction and information fusion. Within the book contributions presenting both novel algorithms and existing applications, especially those (but not restricted to) on-line processing of real world data are included. The areas of Knowledge Extraction and Information Fusion are naturally linked and aim at detecting and estimating the signal of interest and its parameters, and further at combining measurements from multiple sensors (and associated databases if appropriate) to achieve improved accuracies and more specific inferences which cannot be achieved by using only a single signal modality. The subject therefore is of major interest for modern biomedical, environmental, and industrial applications to provide a state of the art and propose new techniques in order to combine heterogeneous information sources.

A Belief Propagation Based Power Distribution System State Estimator

The most popular method used in traditional power system state estimation is the Maximum Likelihood Estimation (MLE). It assumes the state of the system is a set of deterministic variables and determines the most likely state via error included interval measurements. In the distribution system, the measurements are often too sparse to fulfill the system observability. Instead of introducing pseudo-measurements, we propose a Belief Propagation (BP) based distribution system state estimator. This new approach assumes that the system state is a set of stochastic variables. With a set of prior distributions, it calculates the posterior distributions of the state variables via real-time sparse measurements from both traditional measurements and the high resolution smart metering data. In this paper we discuss the step-by-step method of applying the BP algorithm on the distribution system state estimation problem. Our approach provides a seamless connection from the monitoring of transmission system to the feeder circuit, thus filling in the gap between the traditional energy management system (EMS) and the micro-grid customer level optimization. Furthermore, the proposed state estimator can not only be applied to the multi-level electrical coupled grid, but also accommodate the spatial-temporal model for the correlated distributed renewable energy resources. It provides a way of integrating the distributed renew able energy management system into the Smart-Grid Distribution Management System (DMS) and automated substations.

Data fusion for modern engineering applications: an overview

An overview of data fusion approaches is provided from the signal processing viewpoint. The general concept of data fusion is introduced, together with the related architectures, algorithms and performance aspects. Benefits of such an approach are highlighted and potential applications are identified. Case studies illustrate the merits of applying data fusion concepts in real world applications.

A novel augmented complex valued kernel LMS

A novel class of complex valued kernel least mean square (CKLMS) algorithms is introduced with the aim to provide physical meaning to the mapping between the primal and dual space termed the independent CKLMS (iCKLMS). The general class of CKLMS algorithms is also extended in the widely linear sense to develop online kernel algorithms suitable for the processing of general complex valued signals, both circular and noncircular. The so-introduced augmented complex kernel least mean square (ACKLMS) algorithms are verified on adaptive prediction of nonlinear and nonstationary complex wind signals.

GRIP - Grids with intelligent periphery: Control architectures for Grid2050 π

A distributed control and coordination architecture for integrating inherently variable and uncertain generation is presented. The key idea is to distribute the intelligence into the periphery of the grid. This will allow coordination of generation, storage, and adjustable demand on the distribution side of the system and thus reduce the need to build new transmission facilities to accommodate large amounts of renewable generation.

Adaptive kernel methods for CDMA systems

This paper discusses a new adaptive learning approach for code division multiple access (CDMA) systems. The author extends the previous work of Gong et al. (1999) where they applied support vector machines (SVM) for CDMA signal recovery using a modified version of SVM based on a mean squared error criterion called least squares SVM. The least squares SVM solution is found by solving a set of linear equations. An advantage of this formulation is that the algorithm can be implemented adaptively online. The least squares SVM solutions are compared via simulations to other conventional CDMA receivers and found to have comparable performance to standard SVM solutions. The least squares SVM are promising as they offer simple methods of realizing nonlinear receivers, can be implemented adaptively, and can work in time-varying environments that are typical for wireless communications.

Real-time state estimation on micro-grids

This paper presents a new probabilistic approach of the real-time state estimation on the micro-grid. The grid is modeled as a factor graph which can characterize the linear correlations among the state variables. The factor functions are defined for both the circuit elements and the renewable energy generation. With the stochastic model, the linear state estimator conducts the Belief Propagation algorithm on the factor graph utilizing real-time measurements from the smart metering devices. The result of the statistical inference presents the optimal estimates of the system state. The new algorithm can work with sparse measurements by delivering the optimal statistical estimates rather than the solutions. In addition, the proposed graphical model can integrate new models for solar/wind correlation that will help with the integration study of renewable energy. Our state-of-art approach provides a robust foundation for the smart grid design and renewable integration applications.

Support vector machine for multiuser detection in CDMA communications

We apply support vector machines (SVM) or optimal margin classifiers to multiuser detection problems. SVM are well suited for multiuser detection problems as they are based on principles of statistical learning theory where the goal is to construct a maximum margin classifier. We show that a linear SVM converges to the MMSE receiver in the noiseless case. The SVM are also modified to construct nonlinear receivers by using kernel functions and they approximate optimal nonlinear multiuser detection receivers. Using the sequential minimization optimization (SMO) algorithm, we implement SVM as receivers in CDMA systems and compare SVM with traditional and adaptive receivers. The simulation performance of SVM compares favorably to these receivers.

Applications of complex augmented kernels to wind profile prediction

This paper combines complex signal processing with kernel methods for applications in wind prediction. Specifically, we consider developing least squares kernel algorithms for both complex data and augmented complex data. The augmented complex kernel algorithms have advantages over complex kernel algorithms in both the areas of performance and complexity. Use of kernels also allow implementation of nonlinear algorithms by working in the dual space. We apply our algorithm to wind series time prediction and show that our augmented complex algorithms outperform other complex least square algorithms.