Comparison of Shared and Private L1 Data Memories for an Embedded MPSoC in 28nm FD-SOI

2015 ◽  
Author(s):  
Gregor Sievers ◽  
Julian Daberkow ◽  
Johannes Ax ◽  
Martin Flasskamp ◽  
Wayne Kelly ◽  
...  
Keyword(s):  
L1 Data

Circuit design of a dual-versioning L1 data cache

Integration ◽  
2012 ◽  
Vol 45 (3) ◽  
pp. 237-245 ◽  
Author(s):  
Azam Seyedi ◽  
Adrià Armejach ◽  
Adrián Cristal ◽  
Osman S. Unsal ◽  
Ibrahim Hur ◽  
...  
Keyword(s):  
Circuit Design ◽  
Data Cache ◽  
L1 Data

scholarly journals Evaluation of SCIAMACHY Level-1 data versions using nadir ozone profile retrievals in the period 2003–2011

2018 ◽  
Vol 11 (4) ◽  
pp. 2345-2360 ◽  
Author(s):  
Sweta Shah ◽  
Olaf N. E. Tuinder ◽  
Jacob C. A. van Peet ◽  
Adrianus T. J. de Laat ◽  
Piet Stammes
Keyword(s):  
Pressure Range ◽  
Stratospheric Ozone ◽  
Spectral Shift ◽  
Retrieval Algorithm ◽  
Visible Range ◽  
L1 Data ◽  
Ozone Profile ◽  
Level 1 ◽  
Scanning Imaging ◽  
Radiance Data

Abstract. Ozone profile retrieval from nadir-viewing satellite instruments operating in the ultraviolet–visible range requires accurate calibration of Level-1 (L1) radiance data. Here we study the effects of calibration on the derived Level-2 (L2) ozone profiles for three versions of SCanning Imaging Absorption spectroMeter for Atmospheric ChartograpHY (SCIAMACHY) L1 data: version 7 (v7), version 7 with m-factors (v7mfac) and version 8 (v8). We retrieve nadir ozone profiles from the SCIAMACHY instrument that flew on board Envisat using the Ozone ProfilE Retrieval Algorithm (OPERA) developed at KNMI with a focus on stratospheric ozone. We study and assess the quality of these profiles and compare retrieved L2 products from L1 SCIAMACHY data versions from the years 2003 to 2011 without further radiometric correction. From validation of the profiles against ozone sonde measurements, we find that the v8 performs better than v7 and v7mfac due to correction for the scan-angle dependency of the instrument's optical degradation. Validation for the years 2003 and 2009 with ozone sondes shows deviations of SCIAMACHY ozone profiles of 0.8–15 % in the stratosphere (corresponding to pressure range ∼ 100–10 hPa) and 2.5–100 % in the troposphere (corresponding to pressure range ∼ 1000–100 hPa), depending on the latitude and the L1 version used. Using L1 v8 for the years 2003–2011 leads to deviations of ∼ 1–11 % in stratospheric ozone and ∼ 1–45 % in tropospheric ozone. The SCIAMACHY L1 v8 data can still be improved upon in the 265–330 nm range used for ozone profile retrieval. The slit function can be improved with a spectral shift and squeeze, which leads to a few percent residue reduction compared to reference solar irradiance spectra. Furthermore, studies of the ratio of measured to simulated reflectance spectra show that a bias correction in the reflectance for wavelengths below 300 nm appears to be necessary.

scholarly journals P02.03 Automated cell type specific PD-L1 quantification by artificial intelligence using high throughput bleach & stain 15-marker multiplex fluorescence immunohistochemistry in human cancers

2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A8.2-A9
Author(s):  
NC Blessin ◽  
E Bady ◽  
T Mandelkow ◽  
C Yang ◽  
J Raedler ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Cells ◽  
Immune Cells ◽  
T Cell Subsets ◽  
Immune Microenvironment ◽  
L1 Data ◽  
Tumor Immune Microenvironment ◽  
Immune Phenotypes ◽  
Fluorescence Immunohistochemistry ◽  
Tumor Entities

BackgroundThe quantification of PD-L1 (programmed cell death ligand 1) has been used to predict patient’s survival, to characterize the tumor immune microenvironment, and to predict response to immune checkpoint therapies. However, a framework to assess the PD-L1 status with a high interobserver reproducibility on tumor cells and different types of immune cells has yet to be established.Materials and MethodsTo study the impact of PD-L1 expression on the tumor immune microenvironment and patient outcome, a framework for fully automated PD-L1 quantification on tumor cells and immune cells was established and validated. Automated PD-L1 quantification was facilitated by incorporating three different deep learning steps for the analysis of more than 80 different neoplasms from more than 10’000 tumor specimens using a bleach & stain 15-marker multiplex fluorescence immunohistochemistry panel (i.e., PD-L1, PD-1, CTLA-4, panCK, CD68, CD163, CD11c, iNOS, CD3, CD8, CD4, FOXP3, CD20, Ki67, CD31). Clinicopathological parameter were available for more than 30 tumor entities and overall survival data were available for 1517 breast cancer specimens.ResultsComparing the automated deep-learning based PD-L1 quantification with conventional brightfield PD-L1 data revealed a high concordance in tumor cells (p<0.0001) as well as immune cells (p<0.0001) and an accuracy of the automated PD-L1 quantification ranging from 90% to 95.2%. Across all tumor entities, the PD-L1 expression level was significantly higher in distinct macrophage/dendritic cell (DC) subsets (identified by CD68, CD163, CD11c, iNOS; p<000.1) and in macrophages/DCs located in the Stroma (p<0.0001) as compared to intratumoral macrophages/DC subsets. Across all different tumor entities, the PD-L1 expression was highly variable and distinct PD-L1 driven immune phenotypes were identified based on the PD-L1 intensity on both tumor and immune cells, the distance between non-exhausted T-cell subsets (i.e. PD-1 and CTLA-4 expression on CD3+CD8+ cytotoxic T-cells, CD3+CD4+ T-helper cells, CD3+CD4+FOXP3+ regulatory T-cells) and tumor cells as well as macrophage/(DC) subtypes. In breast cancer, the PD-L1 fluorescence intensity on tumor cells showed a significantly higher predictive performance for overall survival with an area under receiver operating curves (AUC) of 0.72 (p<0.0001) than the percentage of PD-L1+ tumor cells (AUC: 0.54). In PD-L1 positive as well as negative breast cancers a close spatial relationship between T- cell subsets (CD3+CD4±CD8±FOXP3±PD-1±CTLA-4±) and Macrophage/DC subsets (CD68±CD163±CD11c±iNOS) was found prognostic relevant (p<0.0001).ConclusionsIn conclusion, multiplex immunofluorescence PD-L1 assessment provides cutoff-free/continuous PD-L1 data which are superior to the conventional percentage of PD-L1+ tumor cells and of high prognostic relevance. The combined analysis of spatial PD-L1/PD-1 data and more than 20 different immune cell subtypes of the immune tumor microenvironment revealed distinct PD-L1 immune phenotypes.Disclosure InformationN.C. Blessin: None. E. Bady: None. T. Mandelkow: None. C. Yang: None. J. Raedler: None. R. Simon: None. C. Fraune: None. M. Lennartz: None. S. Minner: None. E. Burandt: None. D. Höflmayer: None. G. Sauter: None. S.A. Weidemann: None.

scholarly journals Characteristics of CALIOP attenuated backscatter noise: implication for cloud/aerosol detection

2010 ◽  
Vol 10 (7) ◽  
pp. 17263-17305 ◽  
Author(s):  
D. L. Wu ◽  
J. H. Chae ◽  
A. Lambert ◽  
F. F. Zhang
Keyword(s):  
Feature Detection ◽  
Detection Threshold ◽  
Horizontal Resolution ◽  
Occurrence Frequency ◽  
Orthogonal Polarization ◽  
Color Ratio ◽  
Gridded Dataset ◽  
L1 Data ◽  
Measurement Noises ◽  
532 Nm

Abstract. To study cloud/aerosol features in the upper troposphere and lower stratosphere (UT/LS) with the NASA's A-Train sensors, a research algorithm is developed for a re-gridded CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) Level 1 (L1) backscatter dataset. This paper provides a detailed analysis of the measurement noise of this re-gridded dataset in order to compare the lidar measurements with other collocated measurements (e.g., CloudSat, Microwave Limb Sounder). The re-gridded dataset has a manageable data volume for multi-year analysis. It has a fixed (5 km) horizontal resolution, and the measurement error is derived empirically from the background-corrected backscatter profile on a profile-by-profile basis. The 532-nm and 1064-nm measurement noises, determined from the data at altitudes above 19 km, are analyzed and characterized in terms of the mean (μ), standard deviation (σ), and normalized probability density function (PDF). These noises show a larger variance over landmasses and bright surfaces during day, and in regions with enhanced flux of energetic particles during night, where the instrument's ability for feature detection is slightly degraded. An increasing trend in the nighttime 1064-nm σ appears to be significant, which likely causes the increasing differences in cloud occurrence frequency between the 532-nm and 1064-nm channels. Most of the CALIOP backscatter noise distributions exhibit a Gaussian-like behavior but the nighttime 532-nm perpendicular measurements show multi-Gaussian characteristics. We apply σ – based thresholds to detect cloud/aerosol features in the UT/LS from the subset L1 data. The observed morphology is similar to that from the Level 2 (L2) 05km_CLAY+05km_ALAY product, but the occurrence frequency obtained in this study is slightly lower than the L2 product due to differences in spatial averaging and detection threshold. In the case where the measurement noises of two data sets are different, the normalized PDF has proven useful for quantifying the day-night difference of the CALIOP backscatters, showing higher daytime cloud occurrence frequency in the tropical UT/LS. Other cloud/aerosol properties, such as depolarization ratio and color ratio, can be also evaluated with the PDF method.

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