A comparison of interval methods in symbolic circuit analysis applications

Symbolic circuit analysis using Mathematics A method of symbolic circuit analysis using Mathematica is reported. With the powerful symbolic manipulation capacity of Mathematica, symbolic circuit analysis is significantly simplified. The analysed circuits can be passive or active, with active devices modelled by nullors. Non-trivial circuit analysis examples are presented and the program list is given.

Engineering analysis has undergone a dramatic change in the last 5 years. Computer programs with symbolic analysis, advanced interfaces, and high-level languages have brought advanced mathematics to everyone. Not only have these programs made mathematics more accessible, the programs have also made it easier to communicate the results of our analyses. This presentation will demonstrate several aspect of engineering design: symbolic circuit analysis; inverting analysis into design; simple field computation; complex algebra; documentation and presentation. These new programs significantly multiply an engineer's productivity. >

A new approach to the problem of symbolic circuit analysis of large-scale circuits is presented. The methodology has been implemented in a computer program called SCAPP (symbolic circuit analysis program with partitioning). The method solves the problem by utilizing a hierarchical network approach and the sequence of expressions concept rather than a topological approach and the single expression idea which have dominated symbolic analysis in the past. The result is a linear growth in the number of terms in the symbolic solutions for the network approach versus the exponential growth exhibited by traditional methods. The analysis methodology introduces the concept of the RMNA (reduced modified nodal analysis) matrix. This allows the characterization of symbolic networks in terms of only a small subset of the network variables (external variables) rather than the complete set of variables. The analysis algorithm is most efficient when network partitioning is used. Partitioning results in a reduction in the number of terms in the symbolic solutions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The generalization of Bode’s sensitivity analysis technique for all types of the transfer functions and circuit elements is presented in the paper. The proposed formulae for first- and second-order symbolic sensitivity calculation provide the compact size of obtained expression and have the advantages of cancellation-free sum-of-product terms and matrix-free computation. This is achieved by means of the concept of high order summative cofactors and the generalized parameter extraction method. The proposed technique is implemented in symbolic circuit analysis program Cirsym. Illustrative example on symbolic sensitivity circuit analysis and comparison of the presented technique with the transimpedance method and the method based on the modified Coates flow-graph are given.

It is common to distinguish three main senses of the term “robustness”: (1) Robustness of models; (2) Robustness as stability or insensitivity of output as against variations in parameter values; (3) Robustness as consilience of results from different and independent hypotheses, procedures or sources of evidence. The purpose of this paper is to discuss the last two meanings of robustness, in order to cope with some difficulties with which robustness as consilience is confronted and which have indirect consequences for the problem of scientific realism. On the one hand, robustness regarded as reproducible stability as against perturbations and variations in parameter values (robustness-as-stability) and robustness as consilience of results from different and independent pieces of evidence (robustness-as-consilience) are conceptually distinct. On the other hand, however, robustness-as-stability is a condition of robustness-as-consilience; and the converse holds also: robustness-as-consilience is an essential ingredient of robustness-as-stability. There is no vicious circle here, but a technical-practical synergy, which is at the heart of the experimental method, and which can help us out of the two main problems for robustness-as-consilience.

A sequential method of analysis of nullor-based active network is presented. The symbolic circuit analysis can be performed easily with the knowledge of properties of circuit elements including nullor elements. The practical circuit examples are given to demonstrate the feasibility of the proposed approach.

Parallel to algebraic methods, graphical circuit analysis methods have the advantage of cancellation-free. This paper proposes a graph reduction method for hierarchical symbolic circuit analysis by applying a binary decision diagram (BDD) for data sharing. This method is extended from the Graph-Pair Decision Diagram (GPDD) method which was developed for two-port dependent sources. New graph construction rules for multiple-port dependent sources are introduced, with which large analog circuits can be analyzed hierarchically. The new hierarchical method guarantees the cancellation-free property at each layer of hierarchy. The BDD-based hierarchical analysis method can greatly reduce the analysis complexity of the entire circuit, while the software construction and circuit partition remain easy. The new method is compared to the algebraic hierarchical method based on DDD (Determinant Decision Diagram) which does not have the cancellation-free property. Comparable performance can be achieved with the new method which has the extra cancellation-free property.

The wireless body area network (WBAN) has gaining tremendous attention among researchers and academicians for its envisioned applications in healthcare service. Ultra wideband (UWB) radio technology is considered as excellent air interface for communication among body area network devices. Characterisation and modelling of channel parameters are utmost prerequisite for the development of reliable communication system. The path loss of on-body UWB channel for each frequency band defined in IEEE 802.15.6 standard is experimentally determined. The parameters of path loss model are statistically determined by analysing measurement data. Both the line-of-sight and non-line-of-sight channel conditions are considered in the measurement. Variations of parameter values with the size of human body are analysed along with the variation of parameter values with the surrounding environments. It is observed that the parameters of the path loss model vary with the frequency band as well as with the body size and surrounding environment. The derived parameter values are specific to the particular frequency bands of IEEE 802.15.6 standard, which will be useful for the development of efficient UWB WBAN system.

Symbolic circuit analysis

A possible goal in robust design of dynamic systems is to find a system topology under which the sensitivity of performance to the values of component parameters is minimized. This can provide robust performance in the face of environmental change (e.g., resistance variation with temperature) and/or manufacturing-induced variability in parameter values. In some cases, a topology that is relatively insensitive to parameter variation may allow use of less expensive (looser tolerance) components. Cost of components, in some instances, also depends on whether “standard-sized” components may be used or custom values are required. This is true whether the components are electrical components, mechanical fasteners, or hydraulic fittings. However, using only standard-sized or preferred-value components introduces an additional design constraint. This chapter uses genetic programming to develop bond graphs specifying component topology and parameter values for an example task, designing a passive analog low-pass filter with fifth-order Bessel characteristics. It explores three alternative design approaches. The first uses “standard” GP and evolves designs in which components can take on arbitrary values (i.e., custom design). The second approach adds random noise to each parameter; then, at the end of evolution, for the best design found, it “snaps” its parameter values to a small (component-specific) set of “standard” values. The third approach uses only the small set of allowable standard values throughout the evolutionary process, evaluating each design after addition of noise to each standard parameter value. Then the best designs emerging from each of these three procedures are compared for robustness to parameter variation, evaluating each of them with random perturbations of their parameters. Results indicated that, the third method produced the most robust designs, and the second method was better than the first.

Parameter control mechanisms in evolutionary algorithms (EAs) dynamically change the values of the EA parameters during a run. Research over the last two decades has delivered ample examples where an EA using a parameter control mechanism outperforms its static version with fixed parameter values. However, very few have investigated why such parameter control approaches perform better. In principle, it could be the case that using different parameter values alone is already sufficient and EA performance can be improved without sophisticated control strategies. This paper investigates whether very simple random variation in parameter values during an evolutionary run can already provide improvements over static values.

Parameter control mechanisms in evolutionary algorithms (EAs) dynamically change the values of the EA parameters during a run. Research over the last two decades has delivered ample examples where an EA using a parameter control mechanism outperforms its static version with fixed parameter values. However, very few have investigated why such parameter control approaches perform better. In principle, it could be the case that using different parameter values alone is already sufficient and EA performance can be improved without sophisticated control strategies raising an issue in the methodology of parameter control mechanisms' evaluation. This paper investigates whether very simple random variation in parameter values during an evolutionary run can already provide improvements over static values. Results suggest that random variation of parameters should be included in the benchmarks when evaluating a new parameter control mechanism.

BackgroundThe intraclass correlation coefficient (ICC) is widely used in biomedical research to assess the reproducibility of measurements between raters, labs, technicians, or devices. For example, in an inter-rater reliability study, a high ICC value means that noise variability (between-raters and within-raters) is small relative to variability from patient to patient. A confidence interval or Bayesian credible interval for the ICC is a commonly reported summary. Such intervals can be constructed employing either frequentist or Bayesian methodologies.MethodsThis study examines the performance of three different methods for constructing an interval in a two-way, crossed, random effects model without interaction: the Generalized Confidence Interval method (GCI), the Modified Large Sample method (MLS), and a Bayesian method based on a noninformative prior distribution (NIB). Guidance is provided on interval construction method selection based on study design, sample size, and normality of the data. We compare the coverage probabilities and widths of the different interval methods.ResultsWe show that, for the two-way, crossed, random effects model without interaction, care is needed in interval method selection because the interval estimates do not always have properties that the user expects. While different methods generally perform well when there are a large number of levels of each factor, large differences between the methods emerge when the number of one or more factors is limited. In addition, all methods are shown to lack robustness to certain hard-to-detect violations of normality when the sample size is limited.ConclusionsDecision rules and software programs for interval construction are provided for practical implementation in the two-way, crossed, random effects model without interaction. All interval methods perform similarly when the data are normal and there are sufficient numbers of levels of each factor. The MLS and GCI methods outperform the NIB when one of the factors has a limited number of levels and the data are normally distributed or nearly normally distributed. None of the methods work well if the number of levels of a factor are limited and data are markedly non-normal. The software programs are implemented in the popular R language.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2288-14-121) contains supplementary material, which is available to authorized users.

A cost–benefit analysis of BCG revaccination in the Czech Republic

We synthesize information on parameters useful for managing the hunting of two common mammal species that are important for local people in the Neotropics and Africa:Cuniculus pacaandPhilantomba monticola, respectively. We highlight the scarcity of data available on the parameters needed to manage these two species sustainably. As most of the studies were conducted &gt; 40 years ago, we stress the need to supplement the information available using methodological and technical innovations. In particular, we call for new assessments covering the possible variations in parameter values across the species’ distribution ranges, and covering various anthropogenic contexts, to test density-dependent and compensatory processes that may explain the resilience of these species to hunting.