Vikram Krishnamurthy – författare

A unique guide to the state of the art of tracking, classification, and sensor managementThis book addresses the tremendous progress made over the last few decades in algorithm development and mathematical analysis for filtering, multi-target multi-sensor tracking, sensor management and control, and target classification. It provides for the first time an integrated treatment of these advanced topics, complete with careful mathematical formulation, clear description of the theory, and real-world applications.Written by experts in the field, Integrated Tracking, Classification, and Sensor Management provides readers with easy access to key Bayesian modeling and filtering methods, multi-target tracking approaches, target classification procedures, and large scale sensor management problem-solving techniques. Features include: An accessible coverage of random finite set based multi-target filtering algorithms such as the Probability Hypothesis Density filters and multi-Bernoulli filters with focus on problem solvingA succinct overview of the track-oriented MHT that comprehensively collates all significant developments in filtering and trackingA state-of-the-art algorithm for hybrid Bayesian network (BN) inference that is efficient and scalable for complex classification modelsNew structural results in stochastic sensor scheduling and algorithms for dynamic sensor scheduling and managementCoverage of the posterior Cramer-Rao lower bound (PCRLB) for target tracking and sensor managementInsight into cutting-edge military and civilian applications, including intelligence, surveillance, and reconnaissance (ISR)With its emphasis on the latest research results, Integrated Tracking, Classification, and Sensor Management is an invaluable guide for researchers and practitioners in statistical signal processing, radar systems, operations research, and control theory.

Covering formulation, algorithms and structural results and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. In light of major advances in machine learning over the past decade, this edition includes a new Part V on inverse reinforcement learning as well as a new chapter on non-parametric Bayesian inference (for Dirichlet processes and Gaussian processes), variational Bayes and conformal prediction.

Learn about the state of the art in building artificial membranes and synthetic biological devices, and in constructing mathematical models for their dynamics at multiple time and spatial scales with this comprehensive book. Drawing on recent advances in bioengineering and biochemistry, it describes how to engineer tethered bilayer lipid membranes, bioelectronic interfaces, high-resolution biosensors, and diagnostic devices for non-invasive cellular measurements and electroporation. Multi-physics models combining atomistic (molecular dynamics and coarse-grained molecular dynamics), mesoscopic (Poisson-Nernst-Planck), and macroscopic (reaction-rate theory) dynamics provide a complete structure-to-function description of these devices. Experiments and dynamic models explain how anti-microbial peptides penetrate membranes, how molecular machine biosensors built out of artificial membranes can detect femtomolar concentrations, and how electroporation can be controlled. Supported by atomistic simulation code online, this is essential reading for researchers, students and professionals in bioengineering, chemical engineering, biophysics, applied mathematics, and electrical engineering.