12841371
Implementing the Real-Time Processes of RTPA using Real-Time Java
This paper discusses the implementation of the real-time processes of real-time process algebra (RTPA) using real-time Java. The difficulty in implementing real-time software from formal specifications comes from the fact that some realtime features expressed using a formal specification language cannot be easily transformed into a programming language. The paper shows how a combination of a real-time support library for RTPA (RTPASupportLib) and the recently developed extensions to real-time Java can overcome some of these difficulties. The strategies and techniques employed in implementing RTPA real-time features such as concurrency, interleaving, and interrupt/event/time-driven dispatching of real-time processes are presented. A number of case studies on the implementation of RTPA real-time features show that the approach is a feasible one
21455681
A layered reference model of the brain
Based on the advances of research in cognitive informatics and related fields, this paper attempts to develop a layered reference model of the brain that explains the functional mechanisms and cognitive processes of the natural intelligence. A variety of life functions and cognitive processes have been identified in cognitive informatics, cognitive science, neuropsychology, and neurophilosophy. In order to formally and rigorously describe a comprehensive and coherent set of mental processes and their relationships, an integrated reference model of the brain is established, which encompasses 37 cognitive processes at six layers known as the sensation, memory, perception, action, meta and higher cognitive layers from bottom up. The reference model of the brain may be applied to explain a wide range of physiological, psychological, and cognitive phenomena in cognitive informatics, particularly relationships between the inherited and the acquired life functions, as well as the subconscious and conscious cognitive processes.
21184887
The cognitive processes of abstraction and formal inferences
Theoretical research is predominately an inductive process; while applied research is mainly a deductive process. Both inference processes are based on the cognitive process and means of abstraction. This paper describes the cognitive processes of abstraction and formal inferences such as deduction, induction, abduction, and analogy. The hierarchy of abstraction and the descriptivity of abstract means at different levels are analyzed. A set of mathematical models of formal inference methodologies are developed. Formal descriptions of the five cognitive processes of abstraction and inferences are presented using real-time process algebra (RTPA). Applications of abstraction and formal inferences in dealing with complicated problems in large-scale software system development in software engineering are discussed.
40360434
Measurement of the cognitive functional complexity of software
One of the central problems in software engineering is its inherited complexity. It is recognized that cognitive informatics plays an important role in understanding the fundamental characteristics of software. This paper models the cognitive weights of basic control structures of software, and develops a new concept of cognitive functional size for measuring software complexity. Comparative case studies between the cognitive functional size and physical sizes of 20 programs are conducted. It is found that for a given design, although the physical sizes of software may vary in a wide range, the cognitive functional sizes are much stable and independent from implementation technologies and programming languages. Therefore, the cognitive functional size provides a novel, stable and practical software complexity measurement and is helpful to explain the fundamental nature of software complexity in the phase of design, implementation, or maintenance in software engineering.
16244688
Formal Description of Time Management in Real-Time Operating Systems
This paper describes the formal specification of the time management subsystem of a real-time operating system. Real-time process algebra (RTPA) is adopted to formally specify the system. The architecture, static behaviors, and dynamic behaviors of a CPU time manager are systematically specified that form an abstract model of the system. The formal specifications are implemented in C, on which the system performance can be tested and verified
29450095
Cognitive models of the brain
The human brain is the most complicated organ in the universe and a new frontier yet to be explored in an interdisciplinary approach. Investigation of the brain is a unique problem that requires recursive mental power to explore the brain using the brain. This paper attempts to develop functional and cognitive models of the brain by using cognitive informatics and formal methodologies. This paper adopts a memory-based approach to explore the brain, and to demonstrate that memory is the foundation for any natural intelligence. Structures of memories are explored and cognitive models of the natural intelligence are proposed. Cognitive mechanisms of the brain, including hypotheses and theories on the thinking engine of the brain, long-term memory establishment, and roles of sleep in long-term memory development, are investigated. The models and theories are applied to explain a number of fundamental physiological and psychological phenomena.
5987381
Algorithm performance contest
This contest involved the running and evaluation of computer vision and pattern recognition techniques on different data sets with known groundwidth. The contest included three areas; binary shape recognition, symbol recognition and image flow estimation. A package was made available for each area. Each package contained either real images with manual groundtruth or programs to generate data sets of ideal as well as noisy images with known groundtruth. They also contained programs to evaluate the results of an algorithm according to the given groundtruth. These evaluation criteria included the generation of confusion matrices, computation of the misdetection and false alarm rates and other performance measures suitable for the problems. The paper summarizes the data generation for each area and experimental results for a total of six participating algorithms.
14805900
Shape-based 3D surface correspondence using geodesics and local geometry
This paper describes a new method for determining correspondence between points on pairs of surfaces based on shape using a combination of geodesic distance and surface curvature. An initial sparse set of corresponding points is generated using a shape-based matching procedure. Geodesic interpolation is employed in order to capture the complex surface. In addition, surface correspondence and triangulation are computed simultaneously in a hierarchical way. Results applied to human cerebral cortical surfaces are shown to evaluate the approach.
19364030
The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.
10128966
Detecting image orientation based on low-level visual content
Accurately and automatically detecting image orientation is of great importance in intelligent image processing. In this paper, we present automatic image orientation detection algorithms based on both the luminance (structural) and chrominance (color) low-level content features. The statistical learning support vector machines (SVMs) are used in our approach as the classifiers. The different sources of the extracted image features, as well as the binary classification nature of SVM, require our system to be able to integrate the outputs from multiple classifiers. Both static combiner (averaging) and trainable combiner (also based on SVMs) are proposed and evaluated in this work. Furthermore, two rejection options (regular and re-enforced ambiguity rejections) are employed to improve orientation detection accuracy by sieving out images with low confidence values during the classification. Large amounts of experiments have been conducted on a database of more than 14,000 images to validate our approaches. Discussions and future directions for this work are also addressed at the end of the paper.
10936975
Quantifying Evolving Processes in Multimodal 3D Medical Images
Quantitative measurements of changes in evolving brain pathology, such as multiple sclerosis lesions and brain tumors, are important for clinicians to perform pertinent diagnoses and to help in patient follow-up. Lesions or tumors can vary over time in size, shape, location and composition because of natural pathological processes or the effect of a drug treatment or therapy. In the past, people have used as a quantitative measurement the change in total or regional lesion/tumor volume. In this paper we propose a new model to quantify changes in evolving processes in multimodal 3D medical images. We believe this model reflects changes in pathology more accurately because it simultaneously takes into account information in multiple imaging modalities and the location of lesion/tumor voxels. We demonstrate the effectiveness of this model with experiments on synthetic lesion data.