Digital Signal Processing

Contains research objectives and summary of research on seven research projects.U. S. Navy Office of Naval Research (Contract N00014-75-C-0951)National Science Foundation (Grant ENG71-02319-A02

Racial differences in systemic sclerosis disease presentation: a European Scleroderma Trials and Research group study

Objectives. Racial factors play a significant role in SSc. We evaluated differences in SSc presentations between white patients (WP), Asian patients (AP) and black patients (BP) and analysed the effects of geographical locations.Methods. SSc characteristics of patients from the EUSTAR cohort were cross-sectionally compared across racial groups using survival and multiple logistic regression analyses.Results. The study included 9162 WP, 341 AP and 181 BP. AP developed the first non-RP feature faster than WP but slower than BP. AP were less frequently anti-centromere (ACA; odds ratio (OR) = 0.4, P < 0.001) and more frequently anti-topoisomerase-I autoantibodies (ATA) positive (OR = 1.2, P = 0.068), while BP were less likely to be ACA and ATA positive than were WP [OR(ACA) = 0.3, P < 0.001; OR(ATA) = 0.5, P = 0.020]. AP had less often (OR = 0.7, P = 0.06) and BP more often (OR = 2.7, P < 0.001) diffuse skin involvement than had WP.AP and BP were more likely to have pulmonary hypertension [OR(AP) = 2.6, P < 0.001; OR(BP) = 2.7, P = 0.03 vs WP] and a reduced forced vital capacity [OR(AP) = 2.5, P < 0.001; OR(BP) = 2.4, P < 0.004] than were WP. AP more often had an impaired diffusing capacity of the lung than had BP and WP [OR(AP vs BP) = 1.9, P = 0.038; OR(AP vs WP) = 2.4, P < 0.001]. After RP onset, AP and BP had a higher hazard to die than had WP [hazard ratio (HR) (AP) = 1.6, P = 0.011; HR(BP) = 2.1, P < 0.001].Conclusion. Compared with WP, and mostly independent of geographical location, AP have a faster and earlier disease onset with high prevalences of ATA, pulmonary hypertension and forced vital capacity impairment and higher mortality. BP had the fastest disease onset, a high prevalence of diffuse skin involvement and nominally the highest mortality

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million 95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% 95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

Multilevel character templates for document image decoding

Early work in document image decoding (DID) was based on a bilevel imaging model in which an observed image is formed by passing an ideal bilevel image through a memoryless asymmetric bit-flip channel. While this simple model has proven useful in practice, there are many situations in which the bit-flip channel is an inadequate degradation model. This paper presents a multilevel generalization of the bilevel model in which the pixels of the ideal image are assigned values from a finite set of L discrete “colors ” or levels. Level 0 is a background color and the remaining levels are foreground colors. The observed image is bilevel and is modelled as the output of a memoryless L-input symbol, 2-output symbol channel. The multilevel model is motivated in part by the intuition that pixels in a character image are more or less reliably black, depending on their distance from an edge. In addition, the multilevel model supports both “write-black ” and “write-white” levels, and thus can be used to implement a probabilistic analog of morphological “hit-miss ” filtering. In experiments with the Univ. of Washington UW-II English journal database, the character error rate with multilevel templates (L =4)was about a factor of four less than the error rate with bilevel templates. 1

Document Image Decoding in the UC Berkeley Digital Library

The UC Berkeley Environmental Digital Library Project is one of six university-led projects that were initiated in the fall of 1994 as part of a four-year digital library initiative sponsored by the NSF, NASA and ARPA. The Berkeley project is particularly interesting from a document image analysis perspective because its testbed collection consists almost entirely of scanned materials. As a result, the Berkeley project is making extensive use of document recognition and other image analysis technology to provide content-based access to the collection. The Document Image Decoding (DID) group at Xerox PARC is a member of the Berkeley team and is investigating the application of DID techniques to providing high-quality (accurate and properly structured) transcriptions of scanned documents in the collection. This paper briefly describes the Berkeley project, discusses some of its recognition requirements and presents examples of online structured documents created using DID technology. 1...

Supervised template estimation for document image decoding

An approach to supervised training of character templates from page images and unaligned tran-scriptions is proposed. The template training problem is formulated as one of constrained maximum likelihood parameter estimation within the document image decoding framework. This leads to a three-phase iterative training algorithm consisting of transcription alignment, aligned template esti-mation (ATE) and channel estimation steps. The maximum likelihood ATE problem is shown to be NP-complete and thus an approximate solution approach is developed. An evaluation of the train-ing procedure in a document-specific decoding task using the Univ. of Washington UW-II database of scanned technical journal articles is described

Document Image Decoding Approach to Character Template Estimation

This paper develops an approach to supervised training of character templates frompage images and unaligned transcriptions. The template estimation problem is formulated as one of constrained maximum likelihood parameter estimation within the document image decoding framework. This leads to a two-phase iterative training algorithm consisting of transcription alignment and aligned template estimation (ATE) steps. The maximum likelihood ATE problem is shown to be NP-complete and thus a number of simple suboptimal solutions are developed. The training procedure is illustrated by its use in creating a document-specific decoder for high-accuracy transcription of a large (400 page) text document. Depending on the language model used, the decoder character error rate is a factor of 7--20 less than that of a commercial omni-font OCR program; the best case error rate is 0.036%. Index Terms: document image decoding, Markov models, template estimation, character recognition 1 Submitted Nov. 1995..

Xerox PARC ABSTRACT

An approach to supervised training of document-specific character templates from sample page images and unaligned transcriptions is presented. The template estimation problem is formulated as one of constrained maximum likelihoodparameter estimation within the document image decoding (DID) framework. This leads to a two-phase iterative training algorithm consisting of transcriptionalignment and aligned template estimation (ATE) steps. The ATE step is the heart of the algorithm and involves assigning template pixel colors to maximize likelihoodwhile satisifyinga template disjointness constraint. The training algorithm is demonstrated on a variety of English documents, including newspaper columns, 15 th century books, degraded images of 19 th century newspapers and connected text in a script-like font. Three applications enabled by the training procedure are described — high-accuracy document-specific decoding, transcription error visualization and printer font generation. 1

Document image decoding by heuristic search

This correspondence describes an approach to reducing the computational cost of document image decoding by viewing it as a heuristic search problem. The kernel of the approach is a modi ed dynamic programming (DP) algorithm, called the iterated complete path (ICP) algorithm, that is intended for use with separable source models. A set of heuristic functions are presented for decoding formatted text with ICP. Speedups of 3{25 over DP have been observed when decoding text columns and telephone yellow pages using ICP and the proposed heuristics. I