Non-ideal Situations Research Articles

An Ethos for (In)Justice

Where institutions are well-ordered and governed according to principles identified as necessary for justice, the attitudes and behaviors of citizens are also likely to be affected: they will develop a specific ethos that is appropriate for sustaining that just order. However, the absence of a substantially just basic structure will also take effect on what is considered an appropriate sense of justice suited to dealing with nonideal situations characterized by potentially profound injustice. Where ideal theory can describe an ethos necessary for supporting already just institutions and practices, we require a separate account of an ethos appropriate to nonideal conditions.

Effect of various decontamination treatments against Gram-positive bacteria on chicken stored under differing conditions of temperature abuse

The aim of this research was to compare the effectiveness of various chemical decontamination treatments against pathogenic and spoilage Gram-positive bacteria on poultry during storage under different simulated cold chain disruptions. The effect of dipping treatments (15 min; 20 °C) in aqueous solutions of 12% trisodium phosphate (TSP), 1200 ppm acidified sodium chlorite (ASC), 2% citric acid (CA), 220 ppm peroxyacids (PA) or 50 ppm chlorine dioxide (CD) was tested against Listeria monocytogenes, Staphylococcus aureus, Bacillus cereus and Brochothrix thermosphacta artificially inoculated onto skinless chicken legs. Samples were stored for 120 h under three different simulated temperature abuse conditions: T1 (12 h at 1 ± 1 °C, followed by 6 h at 15 ± 1 °C and then 102 h at 4 ± 1 °C), T2 (18 h at 1 ± 1 °C, 6 h at 15 ± 1 °C and 96 h at 10 ± 1 °C) or T3 (18 h at 4 ± 1 °C, 6 h at 20 ± 1 °C and 96 h at 7 ± 1 °C). Microbiological analyses and pH determinations were carried out at 0, 24, 72 and 120 h of storage. TSP, ASC and CA reduced (P < 0.05) microbial populations, relative to control (untreated) samples, whereas PA and CD caused far less noteworthy reductions, independently of the temperature abuse condition selected. Under condition T1 (mild abuse) TSP exerted the highest antimicrobial effect among the decontaminants tested, while ASC showed the highest effectiveness under conditions T2 and T3 (moderate abuse). These findings extend knowledge about the behaviour of various chemical decontaminants under non-ideal storage situations, which very often occur in the poultry supply chain.

Critical analysis of the Bennett-Riedel attack on secure cryptographic key distributions via the Kirchhoff-Law-Johnson-noise scheme.

Recently, Bennett and Riedel (BR) (http://arxiv.org/abs/1303.7435v1) argued that thermodynamics is not essential in the Kirchhoff-law–Johnson-noise (KLJN) classical physical cryptographic exchange method in an effort to disprove the security of the KLJN scheme. They attempted to demonstrate this by introducing a dissipation-free deterministic key exchange method with two batteries and two switches. In the present paper, we first show that BR's scheme is unphysical and that some elements of its assumptions violate basic protocols of secure communication. All our analyses are based on a technically unlimited Eve with infinitely accurate and fast measurements limited only by the laws of physics and statistics. For non-ideal situations and at active (invasive) attacks, the uncertainly principle between measurement duration and statistical errors makes it impossible for Eve to extract the key regardless of the accuracy or speed of her measurements. To show that thermodynamics and noise are essential for the security, we crack the BR system with 100% success via passive attacks, in ten different ways, and demonstrate that the same cracking methods do not function for the KLJN scheme that employs Johnson noise to provide security underpinned by the Second Law of Thermodynamics. We also present a critical analysis of some other claims by BR; for example, we prove that their equations for describing zero security do not apply to the KLJN scheme. Finally we give mathematical security proofs for each BR-attack against the KLJN scheme and conclude that the information theoretic (unconditional) security of the KLJN method has not been successfully challenged.

Measuring flow in non-ideal conditions for short-term projects: Uncertainties associated with the use of stage-discharge rating curves

Summary The vast majority of hydrological stations are set up such that discharge can be estimated from the sole measurement of water height or stage above a local datum. Hydraulics laws show that in the right conditions there may be a unique and stable relationship between stage and discharge, which can be described by a rating curve. For short-term projects where there may be little choice for station location and time to construct a detailed rating curve, conditions for the use of rating curves may be less than ideal, potentially yielding high uncertainties on hydrologic measurements. This article evaluates uncertainties induced on instantaneous flow rates and cumulative annual flow volumes by the use of one-segmented rating curves in small streams. Uncertainty distributions were obtained by simulating rating curves calculated from random sampling of reference flow and stage data obtained with Doppler flowmeters. Factors tested included the number of manual gauged points, the type of rating curve (power vs polynomial), the use or not of the observed stage-of-zero flow, the spread of gauged points along flow range, and the measurement errors during gauging. Results could vary widely depending on the scenarios tested and sometimes yielded very high uncertainties. The best scenario yielded significant uncertainties on annual cumulative flow volume included between −13% and +14% for the low gradient streams and between −5% and +7% for the higher gradient streams, and for 22 manual gauged points per year. Our results show that, even in the best scenario, very significant uncertainty can result from using one-segmented rating curve in non-ideal situations in the field.

Target-Multiplicity Analysis of CML Processor for Partially-Correlated χ2 Targets

Our scope in this paper is to treat the problem of detecting what is called moderately fluctuating targets when the operating environment is contaminated with a number of outlying targets along with the target under test (mult iple -target situations). The illu mination of this impo rtant class of radar targets by a coherent pulse train, return a train of correlate d pulses with a correlation coefficient in the range 0<ρ<1 (intermediate between SW II & SWI). To achieve this goal, we choo se the OS based type of adaptive detectors owing to its immunity to interfering targets. However, the homogeneous performance of OS technique is always lower than that of the CA scheme. Therefore, it is preferab le to choose the more efficient version, which co mbines the benefits of these two schemes, of the adaptive detectors. This modified version is known as censored mean-level (CM L) in the literature. It imp lements trimmed averaging of a weighted ordered range samp les. Here, the detection performance of the CM L processor is analyzed on the assumption that the radar receiver collects data fro m M successive pulses and the radar system operates in target multip licity environ ments. The primary as well as the secondary interfering targets fluctuates in accordance with χ 2 fluctuation model. SWI and SWII cases represent the situations where the signal is comp letely correlated and completely decorrelated, respectively, fro m pulse to pulse. Exact exp ressions are derived for the detection and false alarm rate perfor mances in nonideal situations. For weak SNR, it is shown that the processor performance imp roves as the correlation coefficient ρ increases and this occurs either in the absence or in the presence of outlying targets. This behavior is rapid ly changed as the SNR beco mes stronger where the p rocessor performance degrades as ρ increases, and the SWII and SWI models emb race all the correlated target cases.

Automatic Interlocking Table Generation for Non-ideal Railway Yards

Interlocking systems that can be regarded as the main decision making component of fixed block railway signaling systems, are directly responsible for ensuring the safe movements of trains. Software of an interlocking system depends on the interlocking table of the related railway yard. Although there are tools for automatic interlocking table generation, these tools generally do not consider non-ideal situations like non-signaled track or non-standard use of switches or signals. In this study, an efficient automatic interlocking table generation tool was proposed considering these non-ideal situations.

Image Enhancement in Transient Lock-In Thermography Through Time Series Reconstruction and Spatial Slope Correction

Lock-in (LI) thermography is a popular thermal-nondestructive-testing technique which, like other active thermographic techniques, requires an external heating stimulus, preferably on a blackened surface. It is not, however, immune to nonideal situations like nonuniform heating and surface emissivity variation. The phase image, to some extent, helps to reduce the effect of these artifacts but is inadequate if the variations are large. For example, a poorly blackened metallic sample with reflecting patches on its surface is very difficult to actively thermograph because of direct reflection from the surface. This paper proposes an image reconstruction algorithm for offline removal of such artifacts. In addition, the proposed algorithm enables LI thermography tests in the transient regime and removes temperature gradients due to nonuniform heating. The algorithm was tested with a mild-steel sample having 20-mm-diameter back-drilled holes at various depths ranging from 0.2 to 7.7 mm, stimulated at 20-, 40-, 50-, 60-, and 80-mHz excitation frequencies. The effect of the total number of heating cycles is also presented.

Effect of substrate structure on the performance of thin-film composite forward osmosis hollow fiber membranes

Forward osmosis (FO) membranes have gained increasingly attention due to their potential advantages for various FO-based wastewater treatment, seawater desalination and power generation. In the present study, an attempt was made to systematically investigate the effects of substrate structure on the formation of the selective layer in a two-step preparation – a phase inversion for a hollow fiber substrate followed by interfacial polymerization for the ultra-thin selective layer, and on the FO performance in non-ideal situations, i.e., the feed stream contains foulants. Experimental results revealed that the substrate surface structure is very important for fabricating a RO-like thin film. A substrate with <300 kDa MWCO is preferred to obtain a good semipermeable skin based on current study. It may not be appropriate to use DI water and a low concentration draw solution (i.e., an ideal situation) to evaluate the FO membrane, as the water permeation through the RO-like skin is the rate-determining step, and the effect of the substrate structure cannot be fully reflected in this ideal situation. In addition, the surface structure of the FO membrane facing the feed is critical as it is directly associated with the tendency of FO membrane fouling, and a relatively dense membrane substrate surface is desirable in the membrane orientation of active layer facing draw solution. An ideal FO membrane should also possess anti-fouling capability or less fouling propensity in addition to high water permeability, low salt permeability and minimized structural parameter.

Appearance-based periocular features in the context of face and non-ideal iris recognition

Developing newer approaches to deal with non-ideal scenarios in face and iris biometrics has been a key focus of research in recent years. The same reason motivates the study of the periocular biometrics as its use has a potential of significantly impacting the iris- and face-based recognition. In this paper, we explore the utility of the various appearance features extracted from the periocular region from different perspectives: (i) as an independent biometric modality for human identification, (ii) as a tool that can aid iris recognition in non-ideal situations in the near infra-red (NIR) spectrum, and (iii) as a possible partial face recognition technique in the visible spectrum. We employ a local appearance-based feature representation, where the periocular image is divided into spatially salient patches, appearance features are computed for each patch locally, and the local features are combined to describe the entire image. The images are matched by computing the distance between the corresponding feature representations using various distance metrics. The evaluation of the periocular region-based recognition and comparison to face recognition is performed in the visible spectrum using the FRGC face dataset. For fusion of the periocular and iris modality, we use the MBGC NIR face videos. We demonstrate that in certain non-ideal conditions encountered in our experiments, the periocular biometrics is superior to iris in the NIR spectrum. Furthermore, we also demonstrate that recognition performance of the periocular region images is comparable to that of face in the visible spectrum.

Compensation effect analysis in DIE method for through-casing measuring formation resistivity

Abstract The measuring technique based on Double-Injection-Electrodes (DIE) and its compensation arithmetic method have been proven to be very useful for eliminating the errors caused by electrode-scale mechanical tolerances in formation resistivity measurement through metal case. In this paper, we found that even minor casing joint or casing corrosion may deteriorate the measurement accuracy. Based on theoretical analysis and self-adaptive goal oriented hp -Finite Element (FE) simulations, the compensation effects of DIE measurement technique were estimated. The calculated results from this measuring method are always close to the real formation resistivity, regardless of whether the metal casing is ideal or not. Meanwhile, large errors occur when recording measurements based on Single-Injection-Electrodes (SIE), since the calculated formation resistivity may provide negative values when casing joint or casing corrosion exists. The Double-Injection-Electrode (DIE) measurement technique is predicted to have good compensation effects in many non-ideal situations with uneven metal casing besides electrode-scale mechanical tolerances.

Seismic interferometry by crosscorrelation and by multidimensional deconvolution: a systematic comparison

SUMMARY Seismic interferometry, also known as Green’s function retrieval by crosscorrelation, has a wide range of applications, ranging from surface wave tomography using ambient noise, to creating virtual sources for improved reflection seismology. Despite its successful applications, the crosscorrelation approach also has its limitations. The main underlying assumptions are that the medium is lossless and that the wave field is equipartitioned. These assumptions are in practice often violated: the medium of interest is often illuminated from one side only, the sources may be irregularly distributed, and, particularly for EM applications, losses may be significant. These limitations may partly be overcome by reformulating seismic interferometry as a multidimensional deconvolution (MDD) process. We present a systematic analysis of seismic interferometry by crosscorrelation and by MDD. We show that for the non-ideal situations mentioned above, the correlation function is proportional to a Green’s function with a blurred source. The source blurring is quantified by a so-called point-spread function which, like the correlation function, can be derived from the observed data (i.e., without the need to know the sources and the medium). The source of the Green’s function obtained by the correlation method can be deblurred by deconvolving the correlation function for the point-spread function. This is the essence of seismic interferometry by MDD. We illustrate the crosscorrelation and MDD methods for controlled-source and passive data applications with numerical examples and discuss the advantages and limitations of both methods.

The Relative Approach Algorithm and its Applications in New Perceptual Models for Noisy Speech and Echo Performance

The most common application in wireless networks still is telephony. Speech quality not only depends on quality and coverage provided by the wireless services, but also on the terminals (mobile phones, wireless devices, IP-terminals), which are often the bottleneck with respect to speech quality and echo performance, especially in non-ideal situations. New testing procedures are required which allow a reliable and objective determination of speech quality as perceived by the user and which are mainly based on human perception. Such procedures can be used to optimize terminal and network characteristics as well as to define limits for performance characteristics. This publication describes new types of perceptually based objective speech quality assessment methods targeted at speech quality in the presence of background noise and echo performance of modern narrow- and wideband terminals by giving an output score similar to subjective tests. The models, their calculation steps, their correlation to the underlying subjective tests and applications are presented.

Robust Constrained CMA Based on a Bayesian Approach under Quadratic Constraint

CMA has been known as blind adaptive beamforming because it requires no knowledge about the signal except that the transmitted signal waveform has a constant envelope. But in practical applications, the constrained CMA degrades in the presence of both signal steering vector errors and interference nonstationarity. In this paper, we propose robust constrained CMA based on a Bayesian approach under the quadratic constraint, which improves the output performance in nonideal situations. The quadratic constraint on the weight can provide excellent robustness to signal steering vector mismatches and to random perturbations in sensor parameters. It is found that robust constrained CMA under quadratic constraint can reduce successfully the output power of internal noise while cancelling the interference enough. The Lagrange multipliers are updated and added at each step. The proposed algorithm offers faster rate, has better interference suppression, and yields higher SINR and better signal capture performance than the constrained CMA. Via computer simulation, it is show that the proposed algorithm achieves a substantially improved performance.

Robust entanglement in antiferromagnetic Heisenberg chains by single-spin optimal control

We demonstrate how near-perfect entanglement (in fact arbitrarily close to maximal entanglement) can be generated between the end spins of an anti-ferromagnetic isotropic Heisenberg chain of length $N$, starting from the ground state in the $N/2$ excitation subspace, by applying a magnetic field along a given direction, acting on a single spin only. Temporally optimal magnetic fields to generate a singlet pair between the two end spins of the chain are calculated for chains up to length 20 using optimal control theory. The optimal fields are shown to remain effective in various non-ideal situations including thermal fluctuations, magnetic field leakage, random system couplings and decoherence. Furthermore, the quality of the entanglement generated can be substantially improved by taking these imperfections into account in the optimization. In particular, the optimal pulse of a given thermal initial state is also optimal for any other initial thermal state with lower temperature.

Corrections to “Transforming Traditional Iris Recognition Systems to Work in Nonideal Situations” [Aug 09 3203-3213

The authors of the above titled paper (ibid., vol 56, no. 8, pp. 3203-3213, Aug. 09) point out that the credits for Figs. 14 and 17 were missing in the final print version. These two figures were edited from the figures in another paper (see ref. [2]) and used to introduce the background of the Multimodal Biometrics Grand Challenge organized by the National Institute of Standards and Technology.

Transforming Traditional Iris Recognition Systems to Work in Nonideal Situations

Under a nonideal situation, the image quality may vary. As a result, the traditional iris recognition systems would not work well. However, these kinds of iris recognition systems have been widely deployed in law enforcement and homeland security. It will be desirable to transform the traditional systems to perform in nonideal situations without a costly update. In this paper, we propose a method that upgrades the traditional iris recognition system to work on nonideal situations. The proposed method takes into consideration not only the effect of image quality but also the segmentation accuracy. It employs video-based image-processing techniques to quickly identify and eliminate the bad quality images from iris videos for further processing. The proposed method is tested on public databases using in-house recognition algorithms and also evaluated using a commercialized system. The research results show that the proposed methods can be used to improve the performance of iris recognition systems in a nonideal situation.

Material-based hydrogen storage

One safety aspect of a material-based hydrogen storage system is the exposure of such system to a high-temperature environment (e.g., a fire) causing an increase in pressure. A simple analysis, based on a material balance, is provided to estimate the effect of temperature on equilibrium storage pressure in a material-based hydrogen storage system. Under certain limited ideal and non-ideal situations and using a linear relationship between the hydrogen in the material phase and vapor phase, simple explicit analytical expression for equilibrium storage pressure at a specified temperature can be easily derived without resorting to complicated numerical techniques.

PERFORMANCE ANALYSIS OF OS STRUCTURE OF CFAR DETECTORS IN FLUCTUATING TARGET ENVIRONMENTS

This paper is intended to the analysis of adaptive radar detectors for partially correlated χ 2 targets. This important class of targets is represented by the so-called moderately fluctuating Rayleigh targets, which, when illuminated by a coherent pulse train, return a train of correlated pulses with a correlation coefficient in the range 0 < ρ< 1 (intermediate between SWII and SWI models). The detection of this type of fluctuating targets is practically of great importance. Since the CFAR detectors represent an attractive class of schemes that can be used to overcome the problem of clutter by adaptively setting their threshold based on local information of total noise power, they are commonly used to decide the presence or absence of the radar target of interest, which is of partially correlated χ 2 type. In addition, the OS based algorithms are chosen to carry out this task owing to their immunity to outlying targets which may be present amongst the contents of the reference window. Moreover, since the large processing time of the single-window OS detector limits its practical applications, our scope here is to analyze the performance of OS modified versions for moderately fluctuating Rayleigh targets in nonideal situations. This analysis includes the single-window as well as the double-window OS detection schemes for the case where the radar receiver postdetection integrates M square-law detected pulses and the signal fluctuation obeys χ 2 statistics with two degrees of freedom. These detectors include the mean-level (ML-), the maximum (MX-) and the minimum (MN-) OS algorithms. Exact formulas for their detection probabilities are derived, in the absence as well as in the presence of spurious targets. The primary and the secondary interfering targets are assumed to be of the moderately fluctuating Rayleigh targets. Swerling's well known cases I and II represent the cases where the signal is completely correlated and completely decorrelated, respectively, from pulse to

Aspects of nonideal Stern–Gerlach experiment and testable ramifications

We critically examine the quantum-mechanical modelling of a measurement process using the Stern–Gerlach (SG) setup in the most general context, probing in particular for nonideal situations, the subtleties involved in the connection between the notion of ‘distinguishability’ of apparatus states defined in terms of the inner product and the spatial separation among the wave packets emerging from the SG setup. The quantitative studies highlighting some of the unexplored features of this relationship are presented in terms of an appropriately defined measure for the spatial separation between the emerging wave packets. It is also indicated how the effects arising from such departures from the idealness can be empirically tested for different values of the relevant parameters.

Multi-block PLS modeling for L-shape data structures with applications to mixture modeling

In some situations blocks of data are available from different sources. These data matrices often have no common dimension and are connected in a T- or L-shape structure. The example treated in this paper involves mixture modeling where blocks of data are available on the final product properties ( Y), on the process conditions used to manufacture the products ( Z), on the ratios of raw materials used in the formulations ( R) and on the properties of each of those raw materials ( X). In previous research [K. Muteki, J.F. MacGregor, T. Ueda, On the rapid development of new polymer blends: The optimal selection of materials and blend ratios, Industrial Engineering Chemical Research, Published on-line, May 25 (in press)] T- or L-shaped data matrices were combined using ideal or other known mixing rules to achieve a block data structure with a single common dimension as required in regression modeling. In this paper, we present an alternative multi-block PLS regression approach that does not require the use of such mixing rules to combine the data. Rather, it directly builds a multi-block PLS model for the T- or L-shaped structure. The approach is very flexible and provides increased interpretability on the relationships among the blocks. Furthermore, the method is extended to allow for nonlinear modeling between any of the adjacent data blocks, thereby allowing for non-ideal situations where ideal mixing rules do not hold or are unknown, and where the process is nonlinear. The approach is illustrated using data from an industrial coke making operation where many coals having different properties are blended and processed in coke ovens.