A survey and taxonomy of layout compaction algorithms

This paper presents a survey and a taxonomy of layout compaction algorithms, which are an essential part of modern symbolic layout tools employed in VLSI circuit design. Layout compaction techniques are also used in the low-end stages of silicon compilation tools and module generators. The paper addresses the main algorithms used in compaction, focusing on their implementation characteristics, performance, advantages and drawbacks. Compaction is a highly important operation to optimize the use of silicon area, achieve higher speed through wire length minimization, support technology retargeting and also allow the use of legacy layouts. Optimized cells that were developed for a fabrication process with a set of design rules have to be retargeted for a new and more compact process with a different set of design rules

Real-time simulation of construction workers using combined human body and hand tracking for robotic construction worker system

Construction is an inherently less safe sector than other sectors because it exposes workers to harsh and dangerous working environments. The nature of the construction industry results in a comparatively high incidence of serious injuries and death caused by falls from a height, musculoskeletal disorders and being struck by objects. This paper presents a new concept that can tackle this problem in the future. The central hypothesis of this study is that it is possible to eliminate injuries if we move the human construction worker off-site and remotely link his/her motions to a Robotic Construction Worker (RCW) on-site. As a first steppingstone towards this ultimate goal, two systems essential for the RCW were developed in this study. First, a novel system that combines 3D body and hand position tracking was developed to capture the movements of human construction worker. This combination of tracking enables the capture of changes in the orientations and articulations of the entire human body. Second, a real-time simulation system that connects a human construction worker off-site to a virtual RCW was developed to demonstrate the proposed concept in a variety of construction scenarios. The simulation results demonstrate the future viability of the RCW concept and indicate the promise of this system for eliminating the health and safety risks faced by human construction workers

Dataset Development for the Recognition of Construction Equipment from Images

The construction industry, being one of the largest industrial sectors in Canada, has been continually searching for automated methods that can be adopted to monitor the productivity, consistency, quality and safety of its construction work. The automated recognition of construction operational resources (equipment, workers, materials etc.) has played a significant role in achieving the full automation in monitoring and control of the construction sites. Considering that construction equipment is one of the main operational resources in executing construction tasks, this research work is focused on automated recognition of such equipment from on-site images. In order to achieve this, it is first necessary to evaluate the construction equipment recognition performances of existing object recognition methods. The currently available object recognition datasets that are used to validate the existing recognition methods contain only limited categories of objects, where construction equipment are not included. As a result, it is unclear whether these methods could be used to recognize construction equipment from on-site images, especially considering that construction sites are typically dirty, disorderly, and cluttered. To fill this gap, this research work proposes to create a standardized dataset of construction equipment images that can be used to measure the construction equipment recognition performances of existing object recognition methods. Almost 2,000 images have been collected and compiled to create the dataset, which covers 5 common classes of construction equipment (excavator, loader, tractor, compactor and backhoe loader). Each image has been annotated with information concerning the equipment class, identity, location, orientation, occlusion, and labeling of equipment components (bucket, stick, boom etc.). The effectiveness of the dataset has been tested on two common object recognition methods in computer vision. The recognition tests imply that the recognition methods can be adopted comprehensively for the recognition of construction equipment with the dataset developed in this research. The performances of these two methods are further compared on the basis of the recognition tests conducted in this work. The results show that the construction equipment recognition performance of existing object recognition methods can be evaluated with the dataset in a standard, unbiased, and extensive way

VirtualScan: a new compressed scan technology for test cost reduction

This work describes the VirtualScan technology for scan test cost reduction. Scan chains in a VirtualScan circuit are split into shorter ones and the gap between external scan ports and internal scan chains are bridged with a broadcaster and a compactor. Test patterns for a VirtualScan circuit are generated directly by one-pass VirtualScan ATPG, in which multi-capture clocking and maximum test compaction are supported. In addition, VirtualScan ATPG avoids unknown-value and aliasing effects algorithmically without adding any additional circuitry. The VirtualScan technology has achieved successful tape-outs of industrial chips and has been proven to be an efficient and easy-to-implement solution for scan test cost reduction.2004 International Conference on Test, 26-28 October 2004, Charlotte, NC, USA, US