Experiments in indexing multimedia data at multiple levels.

The increasing availability of digital images, video, and audio has created exciting new research challenges on the organization of multimedia data for a variety of purposes. While some of these challenges relate to computational techniques (e.g., automatic extraction of visual features for automatic indexing of visual data), others are conceptual in nature (e.g., design of templates for manual indexing of visual data). The key issues are what to index from the data, how to perform the indexing of the data, and how to organize the indices obtained. The indices used to describe content as well as the organization of those indices have a tremendous impact on applications, particularly on large digital libraries where different types of media need to be stored and accessed. Relevant efforts in this direction include the emerging MPEG-7 standard [5], which aims at standardizing tools for describing multimedia data

Automated construction of a hierarchy of self-organized neural network classifiers

This paper documents an effort to design and implement a neural network-based, automatic classification system which dynamically constructs and trains a decision tree. The system is a combination of neural network and decision tree technology. The decision tree is constructed to partition a large classification problem into smaller problems. The neural network modules then solve these smaller problems. We used a variant of the Fuzzy ARTMAP neural network which can be trained much more quickly than traditional neural networks. The research extends the concept of self-organization from within the neural network to the overall structure of the dynamically constructed decision hierarchy. The primary advantage is avoidance of manual tedium and subjective bias in constructing decision hierarchies. Additionally, removing the need for manual construction of the hierarchy opens up a large class of potential classification applications. When tested on data from real-world images, the automatically generated hierarchies performed slightly better than an intuitive (handbuilt) hierarchy. Because the neural networks at the nodes of the decision hierarchy are solving smaller problems, generalization performance can really be improved if the number of features used to solve these problems is reduced. Algorithms for automatically selecting which features to use for each individual classification module were also implemented. We were able to achieve the same level of performance as in previous manual efforts, but in an efficient, automatic manner. The technology developed has great potential in a number of commercial areas, including data mining, pattern recognition, and intelligent interfaces for personal computer applications. Sample applications include: fraud detection, bankruptcy prediction, data mining agent, scalable object recognition system, email agent, resource librarian agent, and a decision aid agent

Pattern-Based Systems Engineering (PBSE) - Product lifecycle Management (PLM) integration and validation

Mass customization, small lot sizes, reduced cost, high variability of product types and changing product portfolio are characteristics of modern manufacturing systems during life cycle. A direct consequence of these characteristics is a more complex system and supply chain. Product lifecycle management (PLM) and model based system engineering (MBSE) are tools which have been proposed and implemented to address different aspects of this complexity and resulting challenges. Our previous work has successfully implemented a MBSE model into a PLM platform. More specifically, Pattern based system engineering (S* pattern) models of systems are integrated with TEAMCENTER to link and interface system level with component level, and streamline the lifecycle across disciplines. The benefit of the implementation is two folded. On one side it helps system engineers using system engineering models enable a shift from learning how to model to implementing the model, which leads to more effective systems definition, design, integration and testing. On the other side the PLM platform provides a reliable database to store legacy data for future use also track changes during the entire process, including one of the most important tools that a systems engineer needs which is an automatic report generation tool. In the current work, we have configured a PLM platform (TEAMCENTER) to support automatic generation of reports and requirements tables using a generic Oil Filter system lifecycle. There are three tables that have been configured for automatic generation which are Feature definitions table, Detail Requirements table and Stakeholder Feature Attributes table. These tables where specifically chosen as they describe all the requirements of the system and cover all physical behaviours the oil filter system shall exhibit during its physical interactions with external systems. The requirement tables represent core content for a typical systems engineering report. With the help of the automatic report generation tool, it is possible to prepare the entire report within one single system, the PLM system, to ensure a single reliable data source for an organization. Automatic generation of these contents can save the systems engineers time, avoid duplicated work and human errors in report preparation, train future generation of workforce in the lifecycle all the while encouraging standardized documents in an organization

A template-based methodology for efficient microprocessor and FPGA accelerator co-design

Embedded applications usually require Software/Hardware (SW/HW) designs to meet the hard timing constraints and the required design flexibility. Exhaustive exploration for SW/HW designs is a very time consuming task, while the adhoc approaches and the use of partially automatic tools usually lead to less efficient designs. To support a more efficient codesign process for FPGA platforms we propose a systematic methodology to map an application to SW/HW platform with a custom HW accelerator and a microprocessor core. The methodology mapping steps are expressed through parametric templates for the SW/HW Communication Organization, the Foreground (FG) Memory Management and the Data Path (DP) Mapping. Several performance-area tradeoff design Pareto points are produced by instantiating the templates. A real-time bioimaging application is mapped on a FPGA to evaluate the gains of our approach, i.e. 44,8% on performance compared with pure SW designs and 58% on area compared with pure HW designs

Implementing intelligent asset management systems (IAMS) within an industry 4.0 manufacturing environment

9th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2019; Berlin; Germany; 28 August 2019 through 30 August 2019. Publicado en IFAC-PapersOnLine 52(13), p. 2488-2493This paper aims to define the different considerations and results obtained in the implementation in an Intelligent Maintenance System of a laboratory designed based on basic concepts of Industry 4.0. The Intelligent Maintenance System uses asset monitoring techniques that allow, on-line digital modelling and automatic decision making. The three fundamental premises used for the development of the management system are the structuring of information, value identification and risk management

Automatic Analyzer for Iterative Design

The Office of Naval Research Department Of The Navy Contract Nonr 1834 (03) Project NR-064-18