Electrical and Electronic Engineering - Research Publications
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ItemNo Preview AvailableKnowledge Distillation for Feature Extraction in Underwater VSLAM(IEEE, 2023-01-01)
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ItemNo Preview AvailableGranger causality of Gaussian signals from noisy or filtered measurements(Elsevier BV, 2020-01-01)
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ItemNon-Stochastic Private Function Evaluation(IEEE, 2021-04-11)We consider private function evaluation to provide query responses based on private data of multiple untrusted entities in such a way that each cannot learn something substantially new about the data of others. First, we introduce perfect non-stochastic privacy in a two-party scenario. Perfect privacy amounts to conditional unrelatedness of the query response and the private uncertain variable of other individuals conditioned on the uncertain variable of a given entity. We show that perfect privacy can be achieved for queries that are functions of the common uncertain variable, a generalization of the common random variable. We compute the closest approximation of the queries that do not take this form. To provide a trade-off between privacy and utility, we relax the notion of perfect privacy. We define almost perfect privacy and show that this new definition equates to using conditional disassociation instead of conditional unrelatedness in the definition of perfect privacy. Then, we generalize the definitions to multi-party function evaluation (more than two data entities). We prove that uniform quantization of query responses, where the quantization resolution is a function of privacy budget and sensitivity of the query (cf., differential privacy), achieves function evaluation privacy.
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ItemAn Explicit Formula for the Zero-Error Feedback Capacity of a Class of Finite-State Additive Noise Channels(IEEE, 2020)It is known that for a discrete channel with correlated additive noise, the ordinary capacity with or without feedback both equal log q−H(Z), where H(Z)is the entropy rate of the noise process Z and q is the alphabet size. In this paper, a class of finite-state additive noise channels is introduced. It is shown that the zero-error feedback capacity of such channels is either zero or C 0 f = log q - h(Z), where h(Z) is the topological entropy of the noise process. Moreover, the zero-error capacity without feedback is lower-bounded by log q - 2h(Z). We explicitly compute the zero-error feedback capacity for several examples, including channels with isolated errors and a Gilbert-Elliot channel.
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ItemGranger Causality of Gaussian Signals from Quantized Measurements(IEEE, 2020-03-12)An approach is proposed for inferring Granger causality between jointly stationary, Gaussian signals from quantized data. First, a necessary and sufficient rank criterion for the equality of two conditional Gaussian distributions is proved. Assuming a partial finite-order Markov property, sufficient conditions are then derived under which Granger causality between them can be reliably inferred from the second order moments of the quantized processes. This approach does not require the statistics of the underlying Gaussian signals to be estimated, or a system model to be identified.
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ItemConvergence Analysis of Quantized Primal-dual Algorithm in Quadratic Network Utility Maximization Problems(IEEE, 2015)This paper examines the effect of quantized communications on the convergence behavior of the primal-dual algorithm in quadratic network utility maximization problems with linear equality constraints. In our set-up, it is assumed that the primal variables are updated by individual agents, whereas the dual variables are updated by a central entity, called system, which has access to the parameters quantifying the system-wide constraints. The notion of differential entropy power is used to establish a universal lower bound on the rate of exponential mean square convergence of the primal-dual algorithm under quantized message passing between agents and the system. The lower bound is controlled by the average aggregate data rate under the quantization, the curvature of the utility functions of agents, the number of agents and the number of constraints. An adaptive quantization scheme is proposed under which the primal-dual algorithm converges to the optimal solution despite quantized communications between agents and the system. Finally, the rate of exponential convergence of the primal-dual algorithm under the proposed quantization scheme is numerically studied.
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ItemLower Bounds on the Best-Case Complexity of Solving a Class of Non-cooperative Games(Elsevier, 2016)This paper studies the complexity of solving the class G of all N-player non-cooperative games with continuous action spaces that admit at least one Nash equilibrium (NE). We consider a distributed Nash seeking setting where agents communicate with a set of system nodes (SNs), over noisy communication channels, to obtain the required information for updating their actions. The complexity of solving games in the class G is defined as the minimum number of iterations required to find a NE of any game in G with ε accuracy. Using information-theoretic inequalities, we derive a lower bound on the complexity of solving the game class G that depends on the Kolmogorov 2ε-capacity of the constraint set and the total capacity of the communication channels. We also derive a lower bound on the complexity of solving games in G which depends on the volume and surface area of the constraint set.
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ItemOPTIMAL INFINITE HORIZON CONTROL UNDER A LOW DATA RATE 2(Elsevier BV, 2006)
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ItemA DATA-RATE LIMITED VIEW OF ADAPTIVE CONTROL(Elsevier BV, 2006)
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ItemFinite horizon LQ optimal control and computation with data rate constraints(IEEE - Institute of Electrical and Electronic Engineers, 2005)