A software environment for digital signal-processing simulations

If a program is structured using abstract data types as the basic unit of modularity, then that program is much easier to extend or modify. This thesis is illustrated by the staged development of a program to compute prime numbers based on the sieve of Eratosthenes. This paper includes an extensive introduction to the concept of abstract data types and can be used as a tutorial survey. It includes discussions on the use of abstract data types in connection with recent approaches to data abstractions, heirarchical structure, and program design. Abstract data types are an extension and modification of the traditional concept of data type. An abstract data type defines not only a data representation for objects of the type but also the set of operations that can be performed on objects of the type. Furthermore, the abstract data type can protect the data representation from direct access by other parts of the program.

This paper describes a study aimed at identifying different profiles of students' project development processes. Specifically, we assessed the use of abstract data types for the development of knowledge-based projects. The concept of abstract data types was introduced to high school students who took the course Computer Science-Logic Programming. During their studies the students learned and practiced various tools and methods of project development, one of which was based on the use of abstract data types as tools for problem solving and knowledge representation. To this end, a one-day workshop for team development of mini-projects was organized, and the whole development process was audio and video documented, categorized and analyzed. The pro- files of team behavior in the project development process were specified. The analysis of the profiles resulted in identifying four types of project development teams, all of which employed some or- ganizing tool in developing their projects. Two types of the developing teams used abstract data types and two used other methods. The findings indicated that the process of project development of those who used abstract data types was more structured and more organized than others.

Data Types (ADT) are used when creating software systems, in the systems design. Usually we use algebraic specification to specify the operations in a data type. The use of data types is a methodology or style of working which yields improved design when followed. In this paper we study the addition of input-output operations to the algebraic specification of operations over a data type. The motivation is that input-output operations are used in actual implementations. A specification with input-output is more complete than one without it. We need input-output operations in our programs. This justifies the addition of such operations to the specification. We consider the definition of input-output operations in functional programming in particular in Haskell. Our input-output specifications are not exactly equal to Haskell programs although some of them are likely. We specify input-output operations in a form likely to the specification of the other operations. The result is the algebraic specification of the input-output operations for many frequently used data types. The language considered is sufficiently expressive to model all these operations. The technique is illustrated by means of a variety of examples. We started from sequences, continued with sets and finish with dictionaries. The specifications we present in this paper can be used as specifications of methods of ADT definitions in object oriented programing.

BackgroundRobust, extensible and distributed databases integrating clinical, imaging and molecular data represent a substantial challenge for modern neuroscience. It is even more difficult to provide extensible software environments able to effectively target the rapidly changing data requirements and structures of research experiments. There is an increasing request from the neuroscience community for software tools addressing technical challenges about: (i) supporting researchers in the medical field to carry out data analysis using integrated bioinformatics services and tools; (ii) handling multimodal/multiscale data and metadata, enabling the injection of several different data types according to structured schemas; (iii) providing high extensibility, in order to address different requirements deriving from a large variety of applications simply through a user runtime configuration.MethodsA dynamically extensible data structure supporting collaborative multidisciplinary research projects in neuroscience has been defined and implemented. We have considered extensibility issues from two different points of view. First, the improvement of data flexibility has been taken into account. This has been done through the development of a methodology for the dynamic creation and use of data types and related metadata, based on the definition of “meta” data model. This way, users are not constrainted to a set of predefined data and the model can be easily extensible and applicable to different contexts. Second, users have been enabled to easily customize and extend the experimental procedures in order to track each step of acquisition or analysis. This has been achieved through a process-event data structure, a multipurpose taxonomic schema composed by two generic main objects: events and processes. Then, a repository has been built based on such data model and structure, and deployed on distributed resources thanks to a Grid-based approach. Finally, data integration aspects have been addressed by providing the repository application with an efficient dynamic interface designed to enable the user to both easily query the data depending on defined datatypes and view all the data of every patient in an integrated and simple way.ResultsThe results of our work have been twofold. First, a dynamically extensible data model has been implemented and tested based on a “meta” data-model enabling users to define their own data types independently from the application context. This data model has allowed users to dynamically include additional data types without the need of rebuilding the underlying database. Then a complex process-event data structure has been built, based on this data model, describing patient-centered diagnostic processes and merging information from data and metadata. Second, a repository implementing such a data structure has been deployed on a distributed Data Grid in order to provide scalability both in terms of data input and data storage and to exploit distributed data and computational approaches in order to share resources more efficiently. Moreover, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.ConclusionsBased on such repository, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.

The use of abstract data types for high-level synthesis is addressed. It is demonstrated how data types can be used in the synthesis process to achieve reductions in area and delay of the logic level implementation. Four fundamental type constructs-structures, unions, vectors, and enumerations-are considered. Data types and functions on their values are extended to support don't care information. Supporting abstract data types in behavioral representations is addressed for both an internal data-flow representation and the ELLA hardware description language. Finally, synthesis results for several design examples are presented. Smaller and faster implementations are obtained by supporting encoding and don't care minimization in the high-level synthesis process. >

Structural semantics for polymorphic data types (preliminary report)

Visual diagrammatic programming and block diagrams have been a valuable tool for systems modeling and simulation for several decades in research, development, and educational purposes. Despite the existing mature commercial and free software tools, there is no such kind of tool for the Python programming environment. Little information concerning algorithms to process block diagrams that represent dynamical systems and simulate the corresponding models can be found in the public domain. This is likely because simulation systems based on block diagram descriptions that have become de facto industry standards use proprietary solutions, even if their origins can be traced back to work done in research centers and universities more than seven decades ago. Therefore, the main contributions of this work can be summarized in: i) the development of an algorithm and its detailed description for processing block diagrams (multigraphs), ii) the development of DiaBloS, an open source software tool for Python that implements a diagrammatic visual programming interface and the associated block diagram processing algorithms. The latter contribution aims at code design simplicity, modularity, portability, extensibility of its functionality, and providing an open source tool for Python that could satisfy educational purposes while contributing to the understanding of algorithms for processing block diagrams. Other goals like performance, interface responsiveness aesthetics and code optimization were intentionally treated as secondary goals. We expect this contribution will not only serve as a basis for new developments in visual programming systems, but also provide a free open tool accessible to educators in many areas involving systems modeling and simulation. The numerical accuracy of DiaBloS has been validated to be the same as that achieved by commercial and open code-based existing integration routines implementing the fixed-step RK45 method. Application examples are also provided to demonstrate its capabilities and simplicity. This work also provides an overview of the evolution of existing graph-based modeling paradigms. This overview could also have a reference value, as it brings together many ideas that are connected, but spread in such a way that it becomes difficult to have a general articulated picture of the developments in the field of graphical programming tools and algorithms for processing multigraphs.

Design and Simulation of Heterogeneous Control Systems using Ptolemy II

A matrix representation of a general single-input single-output (SISO) digital filter structure is addressed, proposing a single matrix that stores the complete description of the filter in a very compact and functional format. The proposed matrix contains the structural information corresponding to the block diagram (BD) connections and, at the same time, it can be seen as a valid computational algorithm to implement the filter in the time domain. With this matrix, the gap between the signal flow graph (SFG) and a bit-true implementation of the filter can be considerably reduced. Finally, simple methods to derive the matrix representation from/onto the block diagram and the state-space (SS) mapping are also described and illustrated with some examples.

Automatic Control Systems, Block Diagrams, and Signal Flow Graphs

A method is presented for increasing the measurement accuracy of GMR (giant magnetoresistive) sensors by numerically eliminating the hysteresis in the output signal. A simplified mathematical model of the hysteresis has been derived from the T(x) hysteresis model for anticipating the measurement values. The model has been implemented in a software simulation environment and compared with real GMR sensor measurements, with very good results. Further, the model has been quantised and implemented on a fixed-point digital signal controller (DSC), connected to the output of the sensors. The linear output characteristic delivered in this case by the DSC confirmed the accuracy of the hysteresis model in the hardware implementation too. An algorithm based on the model has also been developed in order to eliminate error propagation during the measurements.

The growth of various software architectural frameworks and models provides a standard governing structure for different types of organizations. Selection of a suitable framework for a particular environment needs much more detailed information in various aspects and a reference guide of features should be provided. This paper brings out the history of software architecture with a new evolution tree. It also technically analyses well known frameworks used in industries and other governmental organizations and lists out the supportive tools for them. This paper presents the comparative chart that can be used as a reference guide to understand top level frameworks and to further research to enable and promote the utilization of these frameworks in various environments. The basic principles of 'software architecture' have been applied since the mid 1980's and it crossed various stages from algorithm's era by borrowing the concepts from others to get a shaped form. In 1928, An Algorithm was formulated to solve the problem by the finite sequence of instructions. Von Neumann developed 'Flow Chart' that has a visual representation of the instruction flow, to plan computer programs in 1947 by inheriting the idea from the flow process chart(1921) and multi flow chart(1944)which were used mostly in the area of electrical engineering. But, there is a gap to point out the flow of control. So, 'Control Flow Diagram' (CFD) was developed in the late 1950's to describe the control flow of a business process and program. This was not enough to view the complex systems. The high level view of the work and immediate access of particular points can't be represented using this diagram. So, to reveal the entire system by dividing into blocks, 'Block Diagram' was developed in late 1950's. A specific function for each block and the connection between blocks will be shown in a diagram. The introduction of abstraction concept became a booster in the field of software architecture. It made a revolution and tremendous growth to that area. By that way, data structures that have similar behaviour, data structures that have similar behaviour, certain data types and modules of one or more programming languages that have similar semantics are grouped in the late 1960's.This was happened by the introduction of Abstract data types. It again leads to 'Modular Programming' that introduces the concept of separate parts called modules in software in 1968. Separation of concerns with the logical boundaries between components is called as modules. In 1977, 'Three Schema Approach' that adopts layered architecture based on the modular programming was developed. It is used to build information systems using three different views in systems development. Here an application will be broken into tiers and developers have to modify a

C++ is not an object—oriented language. It allows for object—oriented programming. The features of object—oriented programming introduced in chapter 9 can be mapped to features of the C++—language. In this chapter we introduce the design of classes in C++ and show the use of abstract data types for realizing encapsulation.KeywordsClass DesignString ClassVector ClassObject ConstructionAbstract Data TypeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A blind identification method for non-linear Wiener models is investigated. When the input signal of the system does not adopt a Gaussian random signal, the identification process with the input signal is changed into the one without input signal by using the first-order statistical properties of the cyclostationary input signal and the inverse mapping of the non-linear part of the model initially, moreover, all internal variables are recovered only based on the output signal. Then, the estimates of the order and parameters of the model are obtained by using the support vector machine regression theory and the higher-order cumulants principle. Finally, compared with other methods, the simulation results show the effectiveness of the proposed method for identifying non-linear Wiener models.

Reliability of the transmission-type optical smoke detectors is limited by the high sensitivity of optical components to parasitic effects, especially to contamination and temperature. Influences of the high rate temperature variations cannot be eliminated by filtering, as their spectral range partially overlaps with the spectral range of a smoke absorption index. Hence, sensitivity to these effects has to be eliminated through the mutual cancellation of the corresponding components of the output signal. First determining the temperature dependence of the light source and the receiver in the generalized block diagram of the transmission-type optical smoke detector, transfer functions are derived, specifying the detector output signal as a function of the temperature variations of the blocks considered. Using the second-order thermal model for the thermal system of the optoelectronic diodes, an expression is given for the steady-state solution of the difference of the source and the receiver diode junction temperatures, when the ambient temperature is a ramp function of time. On the basis of the thermal system analysis, a block diagram is shown to be equivalent to RC low-pass (LP) filters that model the pn junction temperature dependence on ambient temperature. For the completed block diagram, with the help of the expressions derived, an analysis is performed to establish how to eliminate the detector threshold sensitivity to ambient temperature variations, or to adjust it to realize a multicriteria fire detector.