BOARD LEVEL PARTITIONING FOR IMPROVED PARTIAL SCAN

1995 ◽  
Vol 06 (04) ◽  
pp. 573-594
Author(s):  
SPYROS TRAGOUDAS

We present a two phase board level partitioning scheme for improved partial scan on the resulting Integrated Circuits (ICs). The first phase clusters the nodes of the synchronous sequential PCB system into sets of bounded capacity. Each set represents an IC. The main objective function is to minimize the maximum number of inputs to a set. This considerably affects the test generation and response verification phases while testing the ICs. The second phase repositions the flip-flops so that we minimize the partial scan related hardware overhead for each IC, maintain a small sequential depth for all chips, and minimize the period of the global clock. We present an efficient iterative improvement heuristic for the partitioning problem of the first phase whose performance is tested on benchmarks. We also employ provably good algorithms for the second phase which result to reduced hardware overhead for partial scan. The proposed tool may also be applied to the system level partitioning problem where we partition the input circuit into Printed Circuit Boards or Multi-Chip Modules.

2019 ◽  
Vol 2019 (1) ◽  
pp. 000013-000018
Author(s):  
Lance Wang

Abstract The increasing complexity of system on chips (SoCs) combined with a new generation of designs that combine multiple chips in a single package is creating new challenges in the design of packages, printed circuit boards (PCBs) and integrated circuits (ICs). The process typically involves three independent design processes – chip, package and PCB – carried out with point tools whose interface requires time-consuming manual processes that are error-prone and limit the potential for reuse. This challenge is being addressed by a new integrated 3D chip/package/board co-design environment that makes it possible to holistically optimize the package, board and IC design to a greater degree than was possible in the past by considering the system-level impact of each design decision. The new co-design approach enables designers to optimize routability via pin assignment and I/O placement to achieve minimum layer counts between chip, package and board. The end result is higher performance.


Author(s):  
M.G. Burke ◽  
M.K. Miller

Interpretation of fine-scale microstructures containing high volume fractions of second phase is complex. In particular, microstructures developed through decomposition within low temperature miscibility gaps may be extremely fine. This paper compares the morphological interpretations of such complex microstructures by the high-resolution techniques of TEM and atom probe field-ion microscopy (APFIM).The Fe-25 at% Be alloy selected for this study was aged within the low temperature miscibility gap to form a <100> aligned two-phase microstructure. This triaxially modulated microstructure is composed of an Fe-rich ferrite phase and a B2-ordered Be-enriched phase. The microstructural characterization through conventional bright-field TEM is inadequate because of the many contributions to image contrast. The ordering reaction which accompanies spinodal decomposition in this alloy permits simplification of the image by the use of the centered dark field technique to image just one phase. A CDF image formed with a B2 superlattice reflection is shown in fig. 1. In this CDF micrograph, the the B2-ordered Be-enriched phase appears as bright regions in the darkly-imaging ferrite. By examining the specimen in a [001] orientation, the <100> nature of the modulations is evident.


1985 ◽  
Vol 46 (C5) ◽  
pp. C5-251-C5-255
Author(s):  
S. Pytel ◽  
L. Wojnar

Shore & Beach ◽  
2020 ◽  
pp. 92-101
Author(s):  
Richard Raynie ◽  
Syed Khalil ◽  
Charles Villarrubia ◽  
Ed Haywood

The Coastal Protection and Restoration Authority (CPRA) of Louisiana was created after the devastating hurricanes of 2005 (Katrina and Rita) and is responsible for planning and implementing projects that will either reduce storm-induced losses (protection) or restore coastal ecosystems that have been lost or are in danger of being lost (restoration). The first task of the CPRA board was to develop Louisiana’s first Coastal Master Plan (CPRA 2007), which formally integrates and guides the protection and restoration of Louisiana’s coast. The System-Wide Assessment and Monitoring Program (SWAMP) was subsequently developed as a long-term monitoring program to ensure that a comprehensive network of coastal data collection activities is in place to support the planning, development, implementation, and adaptive management of the protection and restoration program and projects within coastal Louisiana. SWAMP includes both natural-system and human-system components and also incorporates the previously-developed Coastwide Reference Monitoring System (CRMS), the Barrier Island Comprehensive Monitoring (BICM) program, and fisheries data collected by the Louisiana Department of Wildlife and Fisheries (LDWF) in addition to other aspects of system dynamics, including offshore and inland water-body boundary conditions, water quality, risk status, and protection performance, which have historically not been the subject of CPRA-coordinated monitoring. This program further facilitates the integration of project-specific data needs into a larger, system-level design framework. Monitoring and operation of restoration and protection projects will be nested within a larger hydrologic basin-wide and coast-wide SWAMP framework and will allow informed decisions to be made with an understanding of system conditions and dynamics at multiple scales. This paper also provides an update on the implementation of various components of SWAMP in Coastal Louisiana, which began as a Barataria Basin pilot implementation program in 2015. During 2017, the second phase of SWAMP was initiated in the areas east of the Mississippi River. In 2019, development of SWAMP design was completed for the remaining basins in coastal Louisiana west of Bayou Lafourche (Figure 1). Data collection is important to inform decisions, however if the data are not properly managed or are not discoverable, they are of limited use. CPRA is committed to ensuring that information is organized and publicly available to help all coastal stakeholders make informed, science-based decisions. As a part of this effort, CPRA has re-engineered its data management system to include spatial viewers, tabular download web pages, and a library/document retrieval system along with a suite of public-facing web services providing programmatic access. This system is collectively called the Coastal Information Management System (CIMS). CPRA and U.S. Geological Survey (USGS) are also developing a proposal to create an interface for CIMS data to be exported to a neutral template that could then be ingested into NOAA’s Data Integration Visualization, Exploration and Reporting (DIVER) repository, and vice versa. DIVER is the repository that the Natural Resource Damage Assessment (NRDA) program is using to manage NRDA-funded project data throughout the Gulf of Mexico. Linking CIMS and DIVER will make it easier to aggregate data across Gulf states and look at larger, ecosystem-level changes.


1995 ◽  
Vol 31 (3-4) ◽  
pp. 25-35 ◽  
Author(s):  
E. M. Rykaart ◽  
J. Haarhoff

A simple two-phase conceptual model is postulated to explain the initial growth of microbubbles after pressure release in dissolved air flotation. During the first phase bubbles merely expand from existing nucleation centres as air precipitates from solution, without bubble coalescence. This phase ends when all excess air is transferred to the gas phase. During the second phase, the total air volume remains the same, but bubbles continue to grow due to bubble coalescence. This model is used to explain the results from experiments where three different nozzle variations were tested, namely a nozzle with an impinging surface immediately outside the nozzle orifice, a nozzle with a bend in the nozzle channel, and a nozzle with a tapering outlet immediately outside the nozzle orifice. From these experiments, it is inferred that the first phase of bubble growth is completed at approximately 1.7 ms after the start of pressure release.


Author(s):  
Yiguang Gong ◽  
Yunping Liu ◽  
Chuanyang Yin

AbstractEdge computing extends traditional cloud services to the edge of the network, closer to users, and is suitable for network services with low latency requirements. With the rise of edge computing, its security issues have also received increasing attention. In this paper, a novel two-phase cycle algorithm is proposed for effective cyber intrusion detection in edge computing based on a multi-objective genetic algorithm (MOGA) and modified back-propagation neural network (MBPNN), namely TPC-MOGA-MBPNN. In the first phase, the MOGA is employed to build a multi-objective optimization model that tries to find the Pareto optimal parameter set for MBPNN. The Pareto optimal parameter set is applied for simultaneous minimization of the average false positive rate (Avg FPR), mean squared error (MSE) and negative average true positive rate (Avg TPR) in the dataset. In the second phase, some MBPNNs are created based on the parameter set obtained by MOGA and are trained to search for a more optimal parameter set locally. The parameter set obtained in the second phase is used as the input of the first phase, and the training process is repeated until the termination criteria are reached. A benchmark dataset, KDD cup 1999, is used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover a pool of MBPNN-based solutions. Combining these MBPNN solutions can significantly improve detection performance, and a GA is used to find the optimal MBPNN combination. The results show that the proposed approach achieves an accuracy of 98.81% and a detection rate of 98.23% and outperform most systems of previous works found in the literature. In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives.


Author(s):  
Tamas Szili-Torok ◽  
Jens Rump ◽  
Torsten Luther ◽  
Sing-Chien Yap

Abstract Better understanding of the lead curvature, movement and their spatial distribution may be beneficial in developing lead testing methods, guiding implantations and improving life expectancy of implanted leads. Objective The aim of this two-phase study was to develop and test a novel biplane cine-fluoroscopy-based method to evaluate input parameters for bending stress in leads based on their in vivo 3D motion using precisely determined spatial distributions of lead curvatures. Potential tensile, compressive or torque forces were not subjects of this study. Methods A method to measure lead curvature and curvature evolution was initially tested in a phantom study. In the second phase using this model 51 patients with implanted ICD leads were included. A biplane cine-fluoroscopy recording of the intracardiac region of the lead was performed. The lead centerline and its motion were reconstructed in 3D and used to define lead curvature and curvature changes. The maximum absolute curvature Cmax during a cardiac cycle, the maximum curvature amplitude Camp and the maximum curvature Cmax@amp at the location of Camp were calculated. These parameters can be used to characterize fatigue stress in a lead under cyclical bending. Results The medians of Camp and Cmax@amp were 0.18 cm−1 and 0.42 cm−1, respectively. The median location of Cmax was in the atrium whereas the median location of Camp occurred close to where the transit through the tricuspid valve can be assumed. Increased curvatures were found for higher slack grades. Conclusion Our results suggest that reconstruction of 3D ICD lead motion is feasible using biplane cine-fluoroscopy. Lead curvatures can be computed with high accuracy and the results can be implemented to improve lead design and testing.


2020 ◽  
Vol 41 (S1) ◽  
pp. s93-s94
Author(s):  
Linda Huddleston ◽  
Sheila Bennett ◽  
Christopher Hermann

Background: Over the past 10 years, a rural health system has tried 10 different interventions to reduce hospital-associated infections (HAIs), and only 1 intervention has led to a reduction in HAIs. Reducing HAIs is a goal of nearly all hospitals, and improper hand hygiene is widely accepted as the main cause of HAIs. Even so, improving hand hygiene compliance is a challenge. Methods: Our facility implemented a two-phase longitudinal study to utilize an electronic hand hygiene reminder system to reduce HAIs. In the first phase, we implemented an intervention in 2 high-risk clinical units. The second phase of the study consisted of expanding the system to 3 additional clinical areas that had a lower incidence of HAIs. The hand hygiene baseline was established at 45% for these units prior to the voice reminder being turned on. Results: The system gathered baseline data prior to being turned on, and our average hand hygiene compliance rate was 49%. Once the voice reminder was turned on, hand hygiene improved nearly 35% within 6 months. During the first phase, there was a statistically significant 62% reduction in the average number of HAIs (catheter associated urinary tract infections (CAUTI), central-line–acquired bloodstream infections (CLABSIs), methicillin-resistant Staphylococcus aureus (MRSA), multidrug-resistant organisms (MDROs), and Clostridiodes difficile experienced in the preliminary units, comparing 12 months prior to 12 months after turning on the voice reminder. In the second phase, hand hygiene compliance increased to >65% in the following 6 months. During the second phase, all HAIs fell by a statistically significant 60%. This was determined by comparing the HAI rates 6 months prior to the voice reminder being turned on to 6 months after the voice reminder was turned on. Conclusions: The HAI data from both phases were aggregated, and there was a statistically significant reduction in MDROs by 90%, CAUTIs by 60%, and C. difficile by 64%. This resulted in annual savings >$1 million in direct costs of nonreimbursed HAIs.Funding: NoneDisclosures: None


2021 ◽  
pp. 136216882110324
Author(s):  
Xabier San Isidro

Despite the numerous attempts to characterize Content and Language Integrated Learning (CLIL), the specialized literature has shown a dearth of cross-contextual studies on how stakeholders conceptualize classroom practice. This article presents the results of a two-phase comparative quantitative study on teachers’ views on CLIL design, implementation and results in two different contexts, Scotland ( n = 127) and Spain ( n = 186). The first phase focused on the creation, pilot-testing and validation of the research tool. The second phase consisted in administering the final questionnaire and analysing the results. The primary goals were (1) to ascertain whether practitioners’ perceptions on CLIL effects and classroom practices match the topics addressed by research; and (2) to analyse and compare teachers’ views in the two contexts. The study offers interesting insights into the main challenges in integrating language and content. Besides providing a conceptual framework for identifiable classroom practice, findings revealed that both cohorts shared broadly similar perceptions, although the Spanish respondents showed more positive views and significantly higher support for this approach.


Author(s):  
Vishu Madaan ◽  
Aditya Roy ◽  
Charu Gupta ◽  
Prateek Agrawal ◽  
Anand Sharma ◽  
...  

AbstractCOVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.


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