Perceptually smooth timbral guides by state-space analysis of phase-vocoder parameters

Sculptor is a phase-vocoder-based package of programs that allows users to explore timbral manipulation of sound in real time. It is the product of a research program seeking ultimately to perform gestural capture by analysis of the sound a performer makes using a conventional instrument. Since the phase-vocoder output is of high dimensionality — typically more than 1,000 channels per analysis frame—mapping phase-vocoder output to appropriate input parameters for a synthesizer is only feasible in theory

Fractional state space analysis of economic systems

This paper examines modern economic growth according to the multidimensional scaling (MDS) method and state space portrait (SSP) analysis. Electing GDP per capita as the main indicator for economic growth and prosperity, the long-run perspective from 1870 to 2010 identifies the main similarities among 34 world partners’ modern economic growth and exemplifies the historical waving mechanics of the largest world economy, the USA. MDS reveals two main clusters among the European countries and their old offshore territories, and SSP identifies the Great Depression as a mild challenge to the American global performance, when compared to the Second World War and the 2008 crisis

Modular State Space Analysis of Coloured Petri Nets

State Space Analysis is one of the most developed analysis methods for Petri Nets. The main problem of state space analysis is the size of the state spaces. Several ways to reduce it have been proposed but cannot yet handle industrial size systems.Large models often consist of a set of modules. Local properties of each module can be checked separately, before checking the validity of the entire system. We want to avoid the construction of a single state space of the entire system.When considering transition sharing, the behaviour of the total system can be capture by the state spaces of modules combined with a Synchronisation Graph. To verify that we do not lose information we show how the full state space can be conctructed.We show how it is possible to determine usual Petri Nets properites, without unfolding to the ordinary state space

State Space Analysis a Tool for Solid Waste Management

Concentration of intense economic processes and high level of consumption in urban areas increase total waste generation and more space is required for waste disposal. Ministry of Finance (BAU: 2009) has estimated by 2041 it would be 1400 sq. km which will be equal to the total area of Mumbai, Chennai and Hyderabad city. Present solid waste management practices are shadowed by institutional lacuna, lack of proper funding, lack of management and operational systems, public apathy, lack of municipal will lead day by day increasing practice of dump to dump yard. The most pressing problem faced by any urban centre in India today is Municipal Solid Waste Management (MSW). Rapid urbanization and changing lifestyles have led to the generation of huge amounts of garbage and waste in the urban areas. Over the past few years, the handling this MSWM has become a major organizational, financial and environmental challenge. (Ramachandra T. V. & Bachmanda, S. 2007). During the last century urban population of India increased ten folds from 27 million to 270 million. India produces 48.0 MT of MSW annually at present. Central Pollution Control Board, India (2009) said that by the year 2021, the urban population is expected to represent 41% of the overall population and subsequently MSW is expected to increase to 300 MT per year, by the year 2047 (490 g to 945 g per capita). A number of technologies are being proposed for management and disposal of garbage but so far no technology has been shortlisted as the one which would be viable not only from the environment angle but also in terms of the cost involved for unanimously in Indian context. (Davidson, 2000) . Waste dumping is the only favorable method to urban local body without any further action. Day by day increasing trend practice of dump to dump yard won’t sustain the function. So there is a requirement of taking integrated policy and technology to use less land as land is precious. A number of technologies are being proposed for reduction of waste quantity through process and disposal of solid waste in general for different city or towns, but so far no technology has been shortlisted as the one which would be viable not only from the environment angle but also in terms of the cost involved for unanimously in urban local body in India. A holistic approach is being therefore, derived through State-Space Model to manage waste by combining and applying a range of suitable techniques, technologies and management programs to achieve less requirement of land near urban areas by accounting area specific number of variables over period of time

State Space Analysis and its Connection to the Classroom

Discrete dynamical systems have been used to theoretically model the complex dynamics of classrooms. While time-series analyses of these models has yielded some insights, state space analyses can yield additional insights; this paper will explore state space analyses and their application to classroom situations. One benefit of state space analysis is that it allows simultaneous exploration of multiple time-series, and so can more easily provide information about divergence and convergence of paths. Additionally, state space analysis, more easily than time-series analysis, can provide information about the existence of multiple paths leading toward a desired state. Further, state space analysis can identify different regimes of behaviors, finding boundaries near which there may be divergent behaviors, and also using those regimes to define a (sometimes) relatively small number of archetypical behaviors. This is particularly useful in tracking behaviors at a microgenetic level, since multiple initial conditions may get to the same (or very close) final states, but in dramatically different ways, and these different routes may have implications for future classroom experiences. Because of these advantages, state space analysis can be used to inform attempts at differentiated instruction in a classroom, assist modelers in identifying appropriate parameter scales, and provide guidance for empirical studies of classroom learning. These ideas will be illustrated through state space analysis of an existing model of teacher-student interactions, identifying four regimes of behaviors, and leading to several implications for classroom practice and research

State-space analysis and synthesis of active RLC networks

This dissertation provides theoretical foundations for the state-space analysis and synthesis of linear active RLC networks containing all four types of controlled sources. The analysis portion is concerned with an explicit representation of the state model and the order of complexity of a general linear active RLC network. The problem considered in the synthesis portion is the realization of a given A-matrix with an active nondegenerate RLC-ladder network or an active RLC network without any particular structure. The general active RLC network state model representations are explicitly expressed in terms of the fundamental circuit submatrices, the network element submatrices, and the dependency submatrices. The branch-capacitor voltages and the chord-inductor currents are uniformly chosen as the state variables. The controlled sources are controlled by the variables of the passive elements and the independent sources. The topological relationships of the network elements and controlled sources appear explicitly in the various submatrices of the state model representations. Such a formulation is helpful in providing an in-depth investigation of the qualitative properties of linear active networks. An algorithm using a unified procedure for evaluating the least upper bound of the order of complexity of a general linear active network is presented. Derivation of this algorithm is based upon the transformation of the original network into an equivalent network containing only resistors, inductors, capacitors, and controlled sources. The algorithm is simple and easy to apply and does not require finding a particular tree or making complicated calculations. The realization of a given A-matrix with an active nondegenerate RLC-ladder network is accomplished by factoring the A-matrix into three matrices. From these matrices, the values of the reactive elements, the topological structure of the passive elements, and the types, locations, and controlling functions of the controlled sources in the realized network are determined. The approach to the realization of a given A-matrix with an active RLC network without any particular structure is to examine the A-matrix for certain properties, identify them, and then synthesize a network from the given A-matrix. Within the general class of active RLC networks, two special classes are considered. Also the sufficient conditions for decomposition of a paramount matrix of order three are established --Abstract, pages ii-iii

State Space Analysis of Dominant Structures in Dynamic Social Systems

Many systems involving human relationships are modeled as dynamic systems, as diverse as urban population growth, diffusion of innovations, spread of viruses, and supply chain management. A fundamental assumption is that these systems contain variables which accumulate and deplete over time (people, innovation adoptions, infections, and orders), and whose dynamics are determined by societal rules and human decision making processes. These assumptions allow the system to be formally expressed by ordinary differential equations which are often nonlinear and contain multiple state variables and feedback loops. Analytical methods have been developed to identify the dominant feedback loops which primarily influence the behavior of the system. However, these dominance methods can produce conflicting results and are often performed in the time-domain under specific initial conditions. This thesis takes a state-space approach to dominance analysis and, in the process, re-examines the definition of dominance. A formal, mathematical definition of dominance is proposed and an analytical procedure is developed and applied to previously studied models. The method produces results consistent with previous analyses and is able explain inconsistencies between other methods. The procedure is then applied in the state-domain and used to identify state-space regions in which certain feedback loops dominate behavior. The procedure is then used to identify the stability properties of equilibria, and a theorem is developed to provide a necessary condition for stability, based on the dominance of balancing (negative) feedback. Lastly, the method is applied to a problem in public health in which a model of the supply and demand of cancer control services is analyzed. The dominant feedback loops are identified for the purpose of revealing potential sources of health disparities between distinct population segments. The analysis revealed the existence of a tipping point condition associated with a single unstable equilibrium point which influences population health outcomes. Furthermore, trajectories near the unstable equilibrium point are dominated by reinforcing (positive) feedback loops which affect the proportion of people seeking cancer control services. These loops result in either virtuous or vicious cycles, depending on which side of the tipping point the system is operating in the state-space. The methods were then used to identify potential leverage points in the system in which small parameter changes cause significant behavior changes. Potential avenues for future dominance research are discussed as well as future transdisciplinary research in public health and implementation science

Dysconnection Topography in Schizophrenia Revealed with State-Space Analysis of EEG

BACKGROUND: The dysconnection hypothesis has been proposed to account for pathophysiological mechanisms underlying schizophrenia. Widespread structural changes suggesting abnormal connectivity in schizophrenia have been imaged. A functional counterpart of the structural maps would be the EEG synchronization maps. However, due to the limits of currently used bivariate methods, functional correlates of dysconnection are limited to the isolated measurements of synchronization between preselected pairs of EEG signals. METHODS/RESULTS: To reveal a whole-head synchronization topography in schizophrenia, we applied a new method of multivariate synchronization analysis called S-estimator to the resting dense-array (128 channels) EEG obtained from 14 patients and 14 controls. This method determines synchronization from the embedding dimension in a state-space domain based on the theoretical consequence of the cooperative behavior of simultaneous time series-the shrinking of the state-space embedding dimension. The S-estimator imaging revealed a specific synchronization landscape in schizophrenia patients. Its main features included bilaterally increased synchronization over temporal brain regions and decreased synchronization over the postcentral/parietal region neighboring the midline. The synchronization topography was stable over the course of several months and correlated with the severity of schizophrenia symptoms. In particular, direct correlations linked positive, negative, and general psychopathological symptoms to the hyper-synchronized temporal clusters over both hemispheres. Along with these correlations, general psychopathological symptoms inversely correlated within the hypo-synchronized postcentral midline region. While being similar to the structural maps of cortical changes in schizophrenia, the S-maps go beyond the topography limits, demonstrating a novel aspect of the abnormalities of functional cooperation: namely, regionally reduced or enhanced connectivity. CONCLUSION/SIGNIFICANCE: The new method of multivariate synchronization significantly boosts the potential of EEG as an imaging technique compatible with other imaging modalities. Its application to schizophrenia research shows that schizophrenia can be explained within the concept of neural dysconnection across and within large-scale brain networks