A model of a microprogrammed functional-oriented computing unit

Digital competencies are important in today's schools. This can mean practical programming skills as well as more conceptual thinking tool developed within the discipline of computer science. Central to my research interests is the specific situation when students encounter programming for the first time. The complex dependencies between practice and theory in the computer lab are analyzed from a pragmatic perspective. My focus is on actions and the situational thinking while doing programming, that is, 'practical thinking'. This situation is being researched both in naturalistic and controlled settings. A pair-programming setting highlights both the interaction with the programming language, the software, and with the Computer Science tradition. One preliminary finding is the importance of resources directed to the problem at hand, including the importance of timing-which could be framed in terms of the Deweyan concept of inquiry.

Representation of spatial information is very important in architecture. Information of a design may be organized as the objects and relations between them. Besides the limitation of those techniques, essentially they are problem specific, the main inefficiency of those methods is that the hierarchical nature of architectural information cannot be represented by them. To combat the inefficiency they have to handle information in a hierarchical manner. In such a representation system not only we can decompose information into smaller pieces also we can focus on the required parts of the problem without involving with many detailed unnecessary information. Also the implicit information in design specifications that is vital must be extracted. Without such information the generated solutions will not be so realistic. The extracted information is essential for hierarchical abstraction of the given design problem. A technique namely the Galois lattice, as a mathematical model, has been used to construct a hierarchical representation scheme using the design specification and to externalize the hidden information in that specification.KeywordsDesign ProblemAbstraction LevelHide InformationFloor PlanHierarchical NatureThese 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.

With the recent industrial expansion, heavy metals and other pollutants have increasingly contaminated our living surroundings. Heavy metals, being non-degradable, tend to accumulate in the food chain, resulting in potentially damaging toxicity to organisms. Thus, techniques to detect metal ions have gradually begun to receive attention. Recent progress in research on synthetic biology offers an alternative means for metal ion detection via the help of promoter elements derived from microorganisms. To make the design easier, it is necessary to develop a systemic design method for evaluating and selecting adequate components to achieve a desired detection performance. A multi-objective (MO) H2/H∞ performance criterion is derived here for design specifications of a metal ion biosensor to achieve the H2 optimal matching of a desired input/output (I/O) response and simultaneous H∞ optimal filtering of intrinsic parameter fluctuations and external cellular noise. According to the two design specifications, a Takagi-Sugeno (T-S) fuzzy model is employed to interpolate several local linear stochastic systems to approximate the nonlinear stochastic metal ion biosensor system so that the multi-objective H2/H∞ design of the metal ion biosensor can be solved by an associated linear matrix inequality (LMI)-constrained multi-objective (MO) design problem. The analysis and design of a metal ion biosensor with optimal I/O response matching and optimal noise filtering ability then can be achieved by solving the multi-objective problem under a set of LMIs. Moreover, a multi-objective evolutionary algorithm (MOEA)-based library search method is employed to find adequate components from corresponding libraries to solve LMI-constrained MO H2/H∞ design problems. It is a useful tool for the design of metal ion biosensors, particularly regarding the tradeoffs between the design factors under consideration.

There has been much debate in the past few years about the appropriate mathematics requirements for an undergraduate computer science major. The discussion has focused primarily on two issues: (1) the underlying mathematical content of computer science courses and (2) the content of mathematics courses which would serve as appropriate cognate requirements for computer science major programs. While this discussion has been helpful, it has been too narrowly focused--it has not started from an understanding of the relationship between the disciplines of mathematics and computer science, but rather has sought to identify mathematical prerequisites that computer science majors need in order to take existing computer science courses. This paper is a small step in seeking to apply an understanding of the relationship between the disciplines of mathematics and computer science to the undergraduate computer science curriculum.

Data acquisition for urban building energy modeling: A review

Many descriptions of the design process do not include the specifications as part of the process, but rather as an input to the design. Once the specifications are considered to be the input, the assumption is made that they are in a form that is suitable for the design process to proceed. The need for a design always starts with ill-defined objectives. The process by which this information is transformed into well-defined design objectives is called the design specification extraction process. An attempt to expose the underlying structure of the first step in the design process—the design specifications—is presented in this paper. The extraction of specifications is divided into four major tasks—diagnosis or information gathering, interpretation and assessment, classification and decomposition, and information patching. The conceptual framework for the design specification extraction and synthesis is implemented in the expert system program SEISD (specification extraction interface for structural design) developed in the LISP programming language. A few examples are presented to demonstrate the implementation strategies. These examples are drawn from the specific problem of a beam design.

Longitudinal statistical analysis has a long past but a short history. In fact, until very recently, longitudinal statistical analysis did not exist as a subject, but was inextricably tied to substantive research in different disciplines. Even today, most publications on longitudinal statistical analysis are written from the perspective of a certain discipline and focus on a specific research design and data structure. Examples include pertinent work on large sample panel data (Hsiao 2014) and single-subject time series data (L€utkepohl 2005) in economics, crossover experimental designs in medical and pharmaceutical research (Jones and Kenward 2014), and the typical applications in the social sciences, often involving multiple individuals and a moderate number of repeated measurement occasions (Singer and Willett 2003). Depending on the field of research and the focus on a specific design or data structure, the history of longitudinal statistical analysis may thus be construed quite differently. This encyclopedia entry is primarily devoted to the history of longitudinal statistical analysis in relation to geropsychology and developmental psychology. As a field, however, modern geropsychology is highly interdisciplinary and thus subject to various influences. As the influences of neighboring disciplines wax and wane, so does the importance of their historical backgrounds. For this reason a description of the history of longitudinal data analysis remains always preliminary, reflecting our present-day view on the most important trends and influences that have shaped the field up to now. As the field changes, the perception of its history may change as well. Taking on today’s notion of the main rationales of longitudinal research and the best way to address them, this encyclopedia entry will sketch the development of the most prominent approaches to longitudinal statistical analysis in geropsychology. Thereby, the focus lies on identifying relevant historical trends rather than presenting a chronology of discrete events. Specific dates mentioned in the following are thus best understood as exemplary milestones of more general developments.

Ramified recurrence and computational complexity III: Higher type recurrence and elementary complexity

Symbolic-Heap Separation logic is a popular formalism for automated reasoning about heap-manipulating programs, which allows the user to give customized data structure definitions. In this paper, we give a new decidability proof for the separation logic fragment of Iosif, Rogalewicz and Simacek. We circumvent the reduction to MSO from their proof and provide a direct model-theoretic construction with elementary complexity. We implemented our approach in the Harrsh analyzer and evaluate its effectiveness. In particular, we show that Harrsh can decide the entailment problem for data structure definitions for which no previous decision procedures have been implemented.

Synthesis of Transfer Function Matrices in Control

Design Recipes for FPGAs

With this paper a new approach for MEMS design tools will be introduced. An analysis of the design tool market leads to the result that most of the designers work with large and inflexible frameworks. Purchasing and maintaining these frameworks is expensive, and gives no optimum support for MEMS design process. The concept of design assistants, carried out with the concept of interacting software components, denotes a new generation of flexible, small, semi-autonomous software systems that are used to solve specific MEMS design tasks in close interaction with the designer. The degree of interaction depends on the complexity of the design task to be performed and the possibility to formalize the respective knowledge. In this context the Internet as one of today's most important communication media provides support for new tool concepts on the basis of the Java programming language. These modern technologies can be used to set up distributed and platform-independent applications. Thus the idea emerged to implement design assistants using Java. According to the MEMS design model new process sequences have to be defined new for every specific design object. As a consequence, assistants have to be built dynamically depending on the requirements of the design process, what can be achieved with component based software development. Componentware offers the possibility to realize design assistants, in areas like design rule checks, process consistency checks, technology definitions, graphical editors, etc. that may reside distributed over the Internet, communicating via Internet protocols. At the University of Siegen a directory for reusable MEMS components has been created, containing a process specification assistant and a layout verification assistant for lithography based MEMS technologies.

To obtain reliable designs, aleatory and epistemic uncertainties are considered recently in the structural analysis and design optimization. The reliability based de-sign optimization (RBDO) method [1] is used when the amount of input data is sufficient enough to create accurate statistical distribution. On the other hand, when the sufficient input data are not available due to limitations in time, human, and facility resources, the optimum design may not be reliable if RBDO method is used. To deal with the situation that input uncertainties have insufficient information, a possibility (or fuzzy set) method can be used for structural analysis and possibility based design optimization (PBDO) [2]. However, in many industry design problems, we may have to deal with design problems that involve with the mixed input statistical random and fuzzy variables simultaneously. For these problems, RBDO may yield unreliable optimum designs because of insufficient data. On the other hand, treating the random variables as fuzzy variables and invoking PBDO to solve the mixed design variable problem may yield too conservative designs with higher optimum costs. This paper proposes a new mixed variable design optimization (MVDO) problem based on the performance measure approach (PMA) [1]. To evaluate the possibilistic constraint in MVDO, a sub-optimization problem for inverse analysis is carried out using a hyper-cylinder domain. To solve this sub-problem efficiently and effectively, a new numerical algorithm, maximum failure search (MFS) method, is proposed in this paper by combining the enhanced hybrid mean value (HMV+) method [3] for the inverse reliability analysis in RBDO and the maximal possibility search (MPS) method [2] for the inverse possibility analysis in PBDO. Some mathematical examples are used to demonstrate the efficiency and effectiveness of the proposed numerical MFS method. Some physical design examples are used to compare the proposed MVDO results with RBDO and PBDO results.

Interpolation and regression of data and smoothing of noisy data play a significant role in many scientific disciplines. Interpolation is an estimation of an unknown variable at output points (locations) by employing the known values at surrounding input points. Regression is an estimation of a variable at both input and output points by employing the known values at the surrounding input locations. Smoothing is an estimation of a variable at only known input points by employing the known input values. Smoothing may be necessary if the input data is noisy. It is worth noting that interpolation is different than regression and smoothing; the estimation based on interpolation passes through all the known input values. In other words, there is an exact recovery of the known values of the input points. There exist several methods for such estimations.

Computers and computing are topics of discussion in many curriculum areas in secondary school. The four courses recommended by this task group, however, have computing as their primary content. The courses are: Introduction to Computer Science I (a full year course)Introduction to Computer Science II (a full year course)Introduction to a High-level Computer Language (a half-year course)Applications and Implications of Computers (a half-year course)Courses 1 and 2 are designed for students with a serious interest in computer science. Course 1 can serve as a single introductory course for some students and also act as a prerequisite for Course 2. At the end of two years of study, students should be prepared to be placed in second level computer science classes in post-secondary educational institutions or to take the Advanced Placement Exam available through the College Entrance Exam Board.Courses 1 and 2 will require a significant amount of equipment for each student enrolled. Computers, either microcomputers or a timesharing system with convenient storage for files (disks) and high-speed printers are necessary.Courses 3 and 4 are designed to be of general interest to any student at the secondary level. Course 3 is a course about programming. It is a general introduction to the process of writing programs in a high-level computer language. It requires extensive hardware facilities, as students will need to practice using whichever computer language is chosen for the instruction.Course 4 includes information about the ways computers are used and about the impact of computer use on people's lives. Because students in this course will be using a computer to learn about applications, teaching this course requires extensive software in addition to hardware.Courses 1, 3 and 4 have been designed assuming no prerequisite courses and are not interdependent.Teachers of all four of these courses should be qualified in the content area and preferably certified to teach about computing and computer science. The discipline of computer science has been evolving over several years and will continue to change. Courses in computer science will similarly continue to change. Well trained teachers are the cornerstone of effective instruction in such a fluid environment.