A technique for monitoring SO2 in combustion exhausts: Use of a non-Nernstian sensing element in combination with an upstream catalytic filter

Detection of sulfur dioxide (SO₂) at high temperature (600–750 °C) in the presence of some interferents found in combustion exhausts (NO₂, NO, CO₂, CO, and hydrocarbon (C₃H₆)) is described. The detection scheme involves use of a catalytic filter in front of a non-Nernstian (mixed-potential) sensing element. The catalytic filter was a Ni:Cr powder bed operating at 850 °C, and the sensing elements were pairs of platinum (Pt) and oxide (Ba-promoted copper chromite ((Ba,Cu)_xCr_yO_z) or Sr-modified lanthanum ferrite (LSF)) electrodes on yttria-stabilized zirconia. The Ni:Cr powder bed was capable of reducing the sensing element response to NO₂, NO, CO, and C₃H₆, but the presence of NO₂ or NO (“NO_x”, at 100 ppm by volume) still interfered with the SO₂ response of the Pt–(Ba,Cu)_xCr_yO_z sensing element at 600 °C, causing approximately a 7 mV (20%) reduction in the response to 120 ppm SO₂ and a response equivalent to about 20 ppm SO₂ in the absence of SO₂. The Pt–LSF sensing element, operated at 750 °C, did not suffer from this NO_x interference but at the cost of a reduced SO₂ response magnitude (120 ppm SO₂ yielded 10 mV, in contrast to 30 mV for the Pt-(Ba,Cu)_xCr_yO_z sensing element). The powder bed and Pt–LSF sensing element were operated continuously over approximately 350 h, and the response to SO₂ drifted downward by about 7%, with most of this change occurring during the initial 100 h of operation.     

Blind separation of signals with mixed kurtosis signs usingthreshold activation functions

A parameterized activation function in the form of an adaptive threshold for a single-layer neural network, which separates a mixture of signals with any distribution (except for Gaussian), is introduced. This activation function is particularly simple to implement, since it neither uses hyperbolic nor polynomial functions, unlike most other nonlinear functions used for blind separation. For some specific distributions, the stable region of the threshold parameter is derived, and optimal values for best separation performance are given. If the threshold parameter is made adaptive during the separation process, the successful separation of signals whose distribution is unknown is demonstrated and compared against other known methods.     

Independent Hybrid Force/Motion Control of Constrained Six-Degrees-of-Freedom Manipulators

Controlling the dynamics of a constrained manipulator includesposition tracking as well as stabilization of the contact wrench.In this paper we derive a control scheme, that makes it possible to treat position and force control independently.The approach is based on a mass-orthogonal splitting of the space of joint torques, allowing independent actuation and therefore independent specification of control laws. An appropriate definition of the reference wrenchmakes it possible to achieve independent stability of the position and force loop.     

What is the optimal architecture for visual information routing?

Analyzing the design of networks for visual information routing is an underconstrained problem due to insufficient anatomical and physiological data. We propose here optimality criteria for the design of routing networks. For a very general architecture, we derive the number of routing layers and the fanout that minimize the required neural circuitry. The optimal fanout l is independent of network size, while the number k of layers scales logarithmically (with a prefactor below 1), with the number n of visual resolution units to be routed independently. The results are found to agree with data of the primate visual system.     

Chipping

Networks dominate today’s computing environment and commercial technical protection is lagging behind attack technology. As a result, protection programme success depends more on prudent management decisions than on the selection of technical safeguards. Managing Network Security takes a management view of protection and seeks to reconcile the need for security with the limitations of technology.     

Why everything keeps failing

Networks dominate today's computing landscape and commercial technical protection is lagging behind attack technology. As a result, protection programme success depends more on prudent management decisions than on the selection of technical safeguards. Managing Network Security takes a management view of protection and seeks to reconcile the need for security with the limitations of technology.     

Lognormal behaviour of untreated and treated wastewater constituents

This article presents an extensive study to select the theoretical probability distribution that represents the concentrations of major constituents from 35 wastewater treatment plants located in Brazil, as well as to verify the influence of the adopted treatment technology on the data distributions. Due to the lack of a solid basis for selecting the distributions that best fit the data, various distributions had to be tried and the choice was based on statistical tests and visual techniques, comprising a total of 3,444 tests undertaken. While comparing suitability of five distributions (normal, lognormal, gamma, exponential and rectangular) for analysis of data (influent and effluent biochemical oxygen demand, chemical oxygen demand, suspended solids, nitrogen, phosphorus, thermotolerant coliforms and flow), it was observed that the lognormal distribution was the most suitable, which is in agreement with the findings from other research studies. This conclusion requires a different position from the one currently adopted when analysing plant performance, in which symmetry of the data is generally implied, which has been shown not to be the case with the large array of data sets investigated.     

Learning invariance from natural images inspired by observations in the primary visual cortex

The human visual system has the remarkable ability to largely recognize objects invariant of their position, rotation, and scale. A good interpretation of neurobiological findings involves a computational model that simulates signal processing of the visual cortex. In part, this is likely achieved step by step from early to late areas of visual perception. While several algorithms have been proposed for learning feature detectors, only few studies at hand cover the issue of biologically plausible learning of such invariance. In this study, a set of Hebbian learning rules based on calcium dynamics and homeostatic regulations of single neurons is proposed. Their performance is verified within a simple model of the primary visual cortex to learn so-called complex cells, based on a sequence of static images. As a result, the learned complex-cell responses are largely invariant to phase and position.