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http://hdl.handle.net/2440/292
2015-04-30T00:38:11ZNew developments in sliding mode control and its applications
http://hdl.handle.net/2440/90788
Title: New developments in sliding mode control and its applications
Author: Wu, L.; Yang, R.; Sun, G.; Zhao, X.; Shi, P.2013-12-31T13:30:00ZApproximate evaluation of marginal association probabilities with belief propagation
http://hdl.handle.net/2440/90690
Title: Approximate evaluation of marginal association probabilities with belief propagation
Author: Williams, J.L.; Lau, R.
Abstract: Data association, the problem of reasoning over correspondence between targets and measurements, is a fundamental problem in tracking. This paper presents a graphical model formulation of data association and applies an approximate inference method, belief propagation (BP), to obtain estimates of marginal association probabilities. We prove that BP is guaranteed to converge, and bound the number of iterations necessary. Experiments reveal a favourable comparison to prior methods in terms of accuracy and computational complexity.2013-12-31T13:30:00ZWas Einstein an engineer?
http://hdl.handle.net/2440/90624
Title: Was Einstein an engineer?
Author: Drake, S.P.
Abstract: Referring to the very epitome of physics as an engineer may appear humorous. However, there is a serious case for it. What would you call somebody who worked in a patent office as a technical expert for seven years, held a number of patents himself on refrigeration, self-adjusting cameras, and electric motors, and explained the photoelectric effect, i.e., the principle now behind photodiodes ? Also, Einstein's father was an engineer-I am not suggesting that engineering is a genetic disease, but engineering was in the family; moreover, Albert Einstein took-and failed-an entrance exam to study electrical engineering in Zurich. Despite being renowned as one of the most abstract thinkers of the 20th century, Einstein was also very interested in the application of ideas, and it could be argued that he was more of an engineer than a physicist, especially in his early career.2014-12-31T13:30:00ZOptimal sensor selection for noisy binary detection in stochastic pooling networks
http://hdl.handle.net/2440/90605
Title: Optimal sensor selection for noisy binary detection in stochastic pooling networks
Author: McDonnell, M.D.; Li, F.; Amblard, P.O.; Grant, A.J.
Abstract: Stochastic Pooling Networks (SPNs) are a useful model for understanding and explaining how naturally occurring encoding of stochastic processes can occur in sensor systems ranging from macroscopic social networks to neuron populations and nanoscale electronics. Due to the interaction of nonlinearity, random noise, and redundancy, SPNs support various unexpected emergent features, such as suprathreshold stochastic resonance, but most existing mathematical results are restricted to the simplest case where all sensors in a network are identical. Nevertheless, numerical results on information transmission have shown that in the presence of independent noise, the optimal configuration of a SPN is such that there should be partial heterogeneity in sensor parameters, such that the optimal solution includes clusters of identical sensors, where each cluster has different parameter values. In this paper, we consider a SPN model of a binary hypothesis detection task and show mathematically that the optimal solution for a specific bound on detection performance is also given by clustered heterogeneity, such that measurements made by sensors with identical parameters either should all be excluded from the detection decision or all included. We also derive an algorithm for numerically finding the optimal solution and illustrate its utility with several examples, including a model of parallel sensory neurons with Poisson firing characteristics.2012-12-31T13:30:00Z