Reverse engineering of printed circuit boards: A conceptual idea

One of the backbones in electronic manufacturing industry is the printed circuit board (PCB).The recent rapid growth in electronics devices, results escalating in the production number of the PCBs.For electronic equipment and appliances which are PCB based, new generations of PCB's are produced to suit the requirements of new products.This development can lead to waste and inefficiency when perfectly serviceable electronic components and appliances have to be scrapped because of the unavailability of spare PCB's from the Original Equipment Manufacturer (OEM) or are already obsolete.This paper proposed a novel framework for reverse engineering of obsolete single layer PCB.Equivalent PCB's which can be used as spares will be reproduced utilizing this new framework.This framework involves several steps, such as Data Acquisition, Image Processing, CAD Editing, PCB Fabrication and Circuitry testing and Analysis.Each stage of the framework and the functionality evaluation of the reproduced PCB will be discussed in detail in following sections

3D survey of the Early-Middle Bronze Age Workshop Complex and cemetery area at Erimi-Laonin tou Porakou (Cyprus)

Kouris Valley Project is an archaeological research project held from 2007 by University of Florence and directed by Prof. A. M. Jasink, under the field direction of Dr. L. Bombardieri in the Kourion area (Limassol, Cyprus). During the 2010 field season the collaboration of GeCo (Geomatics and Communication for Cultural Heritage Laboratory, University of Florence) was requested with the aim at the laser scanner survey of Erimi-Laonin tou Porakou site area. The survey involved two areas, a workshop and a cemetery, as well as some small ceramic finds. Range maps was used for different aims: to make a full-scale replica of a tomb to exhibit at Limassol museum and for the documentation of the excavation of the workshop. This one is a rather extreme case study: most significant artifacts are shallow carvings into bedrock and could be confused with natural elements. In this case the aim was to get automatic and accurate drawings comparable with traditional ones. The paper reviews the expressive potential of drawings done using different techniques and softwares, each one with its assets and restrictions

A structural representation for understanding line-drawing images

International audienceIn this paper, we are concerned with the problem of finding a good and homogeneous representation to encode line-drawing documents (which may be handwritten). We propose a method in which the problems induced by a first-step skeletonization have been avoided. First, we vectorize the image, to get a fine description of the drawing, using only vectors and quadrilateral primitives. A structural graph is built with the primitives extracted from the initial line-drawing image. The objective is to manage attributes relative to elementary objects so as to provide a description of the spatial relationships (inclusion, junction, intersection, etc.) that exist between the graphics in the images. This is done with a representation that provides a global vision of the drawings. The capacity of the representation to evolve and to carry highly semantic information is also highlighted. Finally, we show how an architecture using this structural representation and a mechanism of perceptive cycles can lead to a high-quality interpretation of line drawings

Symbol Recognition: Current Advances and Perspectives

Abstract. The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content

A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&ID)

In the Fourth Industrial Revolution, artificial intelligence technology and big data science are emerging rapidly. To apply these informational technologies to the engineering industries, it is essential to digitize the data that are currently archived in image or hard-copy format. For previously created design drawings, the consistency between the design products is reduced in the digitization process, and the accuracy and reliability of estimates of the equipment and materials by the digitized drawings are remarkably low. In this paper, we propose a method and system of automatically recognizing and extracting design information from imaged piping and instrumentation diagram (P&ID) drawings and automatically generating digitized drawings based on the extracted data by using digital image processing techniques such as template matching and sliding window method. First, the symbols are recognized by template matching and extracted from the imaged P&ID drawing and registered automatically in the database. Then, lines and text are recognized and extracted from in the imaged P&ID drawing using the sliding window method and aspect ratio calculation, respectively. The extracted symbols for equipment and lines are associated with the attributes of the closest text and are stored in the database in neutral format. It is mapped with the predefined intelligent P&ID information and transformed to commercial P&ID tool formats with the associated information stored. As illustrated through the validation case studies, the intelligent digitized drawings generated by the above automatic conversion system, the consistency of the design product is maintained, and the problems experienced with the traditional and manual P&ID input method by engineering companies, such as time consumption, missing items, and misspellings, are solved through the final fine-tune validation process.11Ysciescopu

Analysis of Digital Logic Schematics Using Image Recognition

This thesis presents the results of research in the area of automated recognition of digital logic schematics. The adaptation of a number of existing image processing techniques for use with this kind of image is discussed, and the concept of using sets of tokens to represent the overall drawing i s explained in detail. Methods are given for using tokens to describe schematic component shapes, to represent the connections between components, and to provide sufficient information to a parser so that an equation can be generated. A Microsoft Windows-based test program which runs under Windows 95 or Windows NT has been written to implement the ideas presented. This program accepts either scanned images of digital schematics, or computer-generated images in Microsoft Windows bitmap format as input. It analyzes the input schematic image for content, and produces a corresponding logical equation as output. It also provides the functionality necessary to build and maintain an image token library

A Sustainable Approach for upgrading geographic databases based on high resolution satellite imagery

The availability of high-resolution satellite images could be exploited for upgrading geographic databases at medium scales (1:5,000-1:25,000) as alternative to aerial photogrammetry. The paper presents a procedure to carry out this task which is based on an automatic image-to-image registration procedure of new satellite data to existing ortho-photomaps that have to be upgraded. In order to get a regularization of control points extracted in automatic way, a technique implementing a neural network algorithm is applied. Once an image has been georeferenced, this can be ortho-corrected thanks to a DTM (nowadays available in almost all developed countries). However, the product which is obtained so far is still a raster maps. To cope with the increasing need of vector data in geographic geographic databases, some tests performed on the extraction of features (buildings and roads) from real high-resolution satellite images have been performed and results are shown here. Finally, to complete the data acquisition process, the use of GPS-GIS data-logger receivers in differential mode is proposed

Information Preserving Processing of Noisy Handwritten Document Images

Many pre-processing techniques that normalize artifacts and clean noise induce anomalies due to discretization of the document image. Important information that could be used at later stages may be lost. A proposed composite-model framework takes into account pre-printed information, user-added data, and digitization characteristics. Its benefits are demonstrated by experiments with statistically significant results. Separating pre-printed ruling lines from user-added handwriting shows how ruling lines impact people\u27s handwriting and how they can be exploited for identifying writers. Ruling line detection based on multi-line linear regression reduces the mean error of counting them from 0.10 to 0.03, 6.70 to 0.06, and 0.13 to 0.02, com- pared to an HMM-based approach on three standard test datasets, thereby reducing human correction time by 50%, 83%, and 72% on average. On 61 page images from 16 rule-form templates, the precision and recall of form cell recognition are increased by 2.7% and 3.7%, compared to a cross-matrix approach. Compensating for and exploiting ruling lines during feature extraction rather than pre-processing raises the writer identification accuracy from 61.2% to 67.7% on a 61-writer noisy Arabic dataset. Similarly, counteracting page-wise skew by subtracting it or transforming contours in a continuous coordinate system during feature extraction improves the writer identification accuracy. An implementation study of contour-hinge features reveals that utilizing the full probabilistic probability distribution function matrix improves the writer identification accuracy from 74.9% to 79.5%