Total Dose Effects on Single Event Transients in Linear Bipolar Systems

Single Event Transients (SETs) originating in linear bipolar integrated circuits are known to undermine the reliability of electronic systems operating in the radiation environment of space. Ionizing particle radiation produces a variety of SETs in linear bipolar circuits. The extent to which these SETs threaten system reliability depends on both their shapes (amplitude and width) and their threshold energies. In general, SETs with large amplitudes and widths are the most likely to propagate from a bipolar circuit's output through a subsystem. The danger these SET pose is that, if they become latched in a follow-on circuit, they could cause an erroneous system response. Long-term exposure of linear bipolar circuits to particle radiation produces total ionizing dose (TID) and/or displacement damage dose (DDD) effects that are characterized by a gradual degradation in some of the circuit's electrical parameters. For example, an operational amplifier's gain-bandwidth product is reduced by exposure to ionizing radiation, and it is this reduction that contributes to the distortion of the SET shapes. In this paper, we compare SETs produced in a pristine LM124 operational amplifier with those produced in one exposed to ionizing radiation for three different operating configurations - voltage follower (VF), inverter with gain (IWG), and non-inverter with gain (NIWG). Each configuration produces a unique set of transient shapes that change following exposure to ionizing radiation. An important finding is that the changes depend on operating configuration; some SETs decrease in amplitude, some remain relatively unchanged, some become narrower and some become broader

Total Dose Effects on Error Rates in Linear Bipolar Systems

The shapes of single event transients in linear bipolar circuits are distorted by exposure to total ionizing dose radiation. Some transients become broader and others become narrower. Such distortions may affect SET system error rates in a radiation environment. If the transients are broadened by TID, the error rate could increase during the course of a mission, a possibility that has implications for hardness assurance

The Effects of Low Dose-Rate Ionizing Radiation on the Shapes of Transients in the LM124 Operational Amplifier

Shapes of single event transients (SETs) in a linear bipolar circuit (LM124) change with exposure to total ionizing dose (TID) radiation. SETs shape changes are a direct consequence of TID-induced degradation of bipolar transistor gain. A reduction in transistor gain causes a reduction in the drive current of the current sources in the circuit, and it is the lower drive current that most affects the shapes of large amplitude SETs

Obscuration-dependent evolution of Active Galactic Nuclei

We aim to constrain the evolution of AGN as a function of obscuration using an X-ray selected sample of $\sim2000$ AGN from a multi-tiered survey including the CDFS, AEGIS-XD, COSMOS and XMM-XXL fields. The spectra of individual X-ray sources are analysed using a Bayesian methodology with a physically realistic model to infer the posterior distribution of the hydrogen column density and intrinsic X-ray luminosity. We develop a novel non-parametric method which allows us to robustly infer the distribution of the AGN population in X-ray luminosity, redshift and obscuring column density, relying only on minimal smoothness assumptions. Our analysis properly incorporates uncertainties from low count spectra, photometric redshift measurements, association incompleteness and the limited sample size. We find that obscured AGN with $N_{H}>{\rm 10^{22}\, cm^{-2}}$ account for ${77}^{+4}_{-5}\%$ of the number density and luminosity density of the accretion SMBH population with $L_{{\rm X}}>10^{43}\text{ erg/s}$, averaged over cosmic time. Compton-thick AGN account for approximately half the number and luminosity density of the obscured population, and ${38}^{+8}_{-7}\%$ of the total. We also find evidence that the evolution is obscuration-dependent, with the strongest evolution around $N_{H}\thickapprox10^{23}\text{ cm}^{-2}$. We highlight this by measuring the obscured fraction in Compton-thin AGN, which increases towards $z\sim3$, where it is $25\%$ higher than the local value. In contrast the fraction of Compton-thick AGN is consistent with being constant at $\approx35\%$, independent of redshift and accretion luminosity. We discuss our findings in the context of existing models and conclude that the observed evolution is to first order a side-effect of anti-hierarchical growth.Comment: Published in Ap

Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos

International audienceEvaluating content-based retrieval (CBR) is challenging because it requires an adequate ground-truth. When the available ground-truth is limited to textual metadata such as pathological classes, retrieval results can only be evaluated indirectly, for example in terms of classification performance. In this study we first present a tool to generate perceived similarity ground-truth that enables direct evaluation of endomicroscopic video retrieval. This tool uses a four-points Likert scale and collects subjective pairwise similarities perceived by multiple expert observers. We then evaluate against the generated ground-truth a previously developed dense bag-of-visual-words method for endomicroscopic video retrieval. Confirming the results of previous indirect evaluation based on classification, our direct evaluation shows that this method significantly outperforms several other state-of-the-art CBR methods. In a second step, we propose to improve the CBR method by learning an adjusted similarity metric from the perceived similarity ground-truth. By minimizing a margin-based cost function that differentiates similar and dissimilar video pairs, we learn a weight vector applied to the visual word signatures of videos. Using cross-validation, we demonstrate that the learned similarity distance is significantly better correlated with the perceived similarity than the original visual-word-based distance

Learning Semantic and Visual Similarity for Endomicroscopy Video Retrieval

Traditional Content-Based Image Retrieval (CBIR) systems only deliver visual outputs that are not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval that computes a visual signature for each video. In this study, we first leverage semantic ground-truth data to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that our visual-word-based semantic signatures enable a recall performance which is significantly higher than those of several state-of-the-art methods in CBIR. In a second step, we propose to improve retrieval relevance by learning, from a perceived similarity ground truth, an adjusted similarity distance. Our distance learning method allows to improve, with statistical significance, the correlation with the perceived similarity. Our resulting retrieval system is efficient in providing both visual and semantic information that are correlated with each other and clinically interpretable by the endoscopists

Image-based Semantic Learning Software for Automatic Detection of Discriminative Criteria used for probe-based Confocal Laser Endomicroscopy (pCLE) Diagnosis of Colorectal Polyps

Gastrointestinal EndoscopyBACKGROUND AND AIMS pCLE (Cellvizio, Mauna Kea Technologies) enables in vivo microscopic imaging of the epithelium in real-time during ongoing endoscopy. As pCLE is a recent technology, the in vivo interpretation of pCLE images of colorectal polyps is still challenging for many endoscopists. This study aims at supporting pCLE diagnosis of colorectal polyps, by developing a software based on image retrieval for the automatic extraction of semantic concepts in pCLE sequences. METHODS Intravenous fluorescein pCLE imaging of colorectal lesions was performed on patients undergoing surveillance colonoscopies. The pCLE video sequences, recorded for each polyp, were manually annotated with 6 discriminative criteria either present or absent in the videos. These criteria, annotated by expert endoscopists with the support of the modified Mainz criteria, were: "visible blood vessel", "normal goblet cell", "round crypt", "elongated/tortuous crypt", "visible lumen" and "star-shaped opening". These semantic criteria were then learned by the proposed software based on a content-based image retrieval technique followed by a Fisher-based transformation method. For each discriminative criterion, the performance of automatic detection performed by the proposed software were compared to that of state-of-the-art machine learning methods (support vector machines) using 30x3 fold cross-validation. RESULTS 118 colorectal lesions were imaged in 66 patients. Based on histopathology, 83 of these 118 lesions were neoplastic and 35 were non-neoplastic. Table 1 reports the area under the receiver operating characteristic (ROC) curves indicating the performance of automatic criteria detection. The proposed detection software performs overall statistically better than the state-of-the-art machine learning methods (p < 0.05). Figure 1 shows a typical example of a pCLE query, for which the most visually similar pCLE sequence is automatically extracted, together with "semantic" star plots showing the probability that each discriminative criterion is present in the pCLE sequences. Possible disagreements between automatic criteria detection and ground-truth annotations may reveal ambiguous pCLE sequences that are difficult to interpret. CONCLUSIONS This study is a proof of concept that pCLE clinical knowledge can be automatically extracted from pCLE sequences of colorectal polyps. The proposed software for automatic semantic detection combined with image retrieval provides the endoscopists with clinically relevant information, both visual and semantic, which should be easily interpretable to make an informed pCLE diagnosis. Further studies are needed to improve software performances and to evaluate the software as a second reader tool for pCLE diagnosis

Comparison of a Classification Software based on Image Retrieval with the Off-Line Diagnosis of Expert Endoscopists for probe-based Confocal Laser Endomicroscopy (pCLE) of Colorectal Polyps

Gastrointestinal Endoscopy (DDW 2012)BACKGROUND AND AIMS pCLE (Cellvizio, Mauna Kea Technologies) enables in vivo microscopic imaging of the epithelium in real-time during ongoing endoscopy. An image retrieval software prototype for automatic classification of pCLE images, recently developed to assist the endoscopists in the in vivo pCLE diagnosis of colorectal polyps, has the great potential of decreasing inter-observer agreement while increasing diagnostic performance of endoscopists. This study aims at comparing the performances of the classification software with the performance of pCLE diagnosis established off-line by expert endoscopists. METHODS Intravenous fluorescein pCLE imaging of colorectal lesions was performed on patients undergoing surveillance colonoscopies, followed by polypectomies. Histopathology was used as gold standard for the differentiation between neoplastic and non-neoplastic lesions. The pCLE sequences, recorded for each polyp, were analyzed off-line by 2 expert endoscopists, blinded to the endoscopic characteristics and histopathology. These pCLE videos, along with their histopathology diagnosis, were used to train the classification software which is a content-based image retrieval technique followed by k-nearest neighbor classification. All evaluations were performed using leave-onepatient- out (LOPO) cross-validation to avoid bias. RESULTS 135 colorectal lesions, including 6 serrated adenoma cases, were imaged in 71 patients. Based on histopathology, 93 of these 135 lesions were neoplastic and 42 were non-neoplastic. No statistical significance was found for the difference between the performance of software classification (accuracy 89.6%, sensitivity 92.5%, specificity 83.3%, using LOPO) and the performance of off-line diagnosis of pCLE established by the expert endoscopists (accuracy 89.6%, sensitivity 91.4%, specificity 85.7%). The 95% confidence intervals for equivalence testing (−0.073 to 0.073 for accuracy, −0.068 to 0.089 for sensitivity, −0.18 to 0.13 for specificity) are sufficiently small to suggest statistical equivalence. The −0.18 lower bound for the specificity should be sufficient if the classification software is only taken as a second-reader tool to support pCLE diagnosis. CONCLUSIONS The image retrieval software for automatic classification of pCLE sequences of colorectal polyps achieves a high performance which is statistically comparable to that of off-line diagnosis of pCLE sequences established by expert endoscopists. A fortiori, the classification software should be useful, not only to train non expert endoscopists, but also to assist any endoscopist in in vivo pCLE diagnosis. DISCUSSION The proposed software is not a "black box" but an informative tool based on the query by example model that produces, as intermediate results, visually similar annotated pCLE videos directly interpretable by the endoscopist

Endomicroscopic image retrieval and classification using invariant visual features

International audienceThis paper investigates the use of modern content based image retrieval methods to classify endomicroscopic images into two categories: neoplastic (pathological) and benign. We describe first the method that maps an image into a visual feature signature which is a numerical vector invariant with respect to some particular classes of geometric and intensity transformations. Then we explain how these signatures are used to retrieve from a database the k closest images to a new image. The classification is finally achieved through a procedure of votes weighted by a proximity criterion (weighted k-nearest neighbors). Compared with several previously published alternatives whose maximal accuracy rate is almost 67 % on the database, our approach yields an accuracy of 80 % and offers promising perspectives