Pseudo-noise pulse-compression thermography: a powerful tool for time-domain thermography analysis

Pulse-compression is a correlation-based measurement technique successfully used in many NDE applications to increase the SNR in the presence of huge noise, strong signal attenuation or when high excitation levels must be avoided. In thermography, the pulse-compression approach was firstly introduced in 2005 by Mulavesaala and co-workers, and then further developed by Mandelis and co-authors that applied to thermography the concept of the thermal-wave radar developed for photothermal measurements. Since then, many measurement schemes and applications have been reported in the literature by several groups by using various heating sources, coded excitation signals, and processing algorithms. The variety of such techniques is known as pulse-compression thermography or thermal-wave radar imaging. Even despite the continuous improvement of these techniques during these years, the advantages of using a correlation-based approach in thermography are still not fully exploited and recognized by the community. This is because up to now the reconstructed thermograms' time sequences after pulse-compression were affected by the so-called sidelobes. This is a severe drawback since it hampers an easy interpretation of the data and their comparison with other thermography techniques. To overcome this issue and unleash the full potential of the approach, this paper shows how it is possible to implement a pulse-compression thermography procedure capable of suppressing any sidelobe by using a pseudo-noise excitation and a proper processing algorithm.Comment: 24 paged, 20 figure

Spatial clustering and its effect on perceived clustering, numerosity, and dispersion

Human observers are able to estimate the numerosity of large sets of visual elements. The occupancy model of perceived numerosity in intermediate numerical ranges is based on overlapping regions of influence. The key idea is that items within a certain range count for less than their actual numerical value and more so the closer they are to their neighbours. Therefore occupancy is sensitive to the grouping of elements, but there are other spatial properties of configurations that could also influence perceived numerosity, such as: area of convex hull, occupancy area, total degree of connectivity, and local clustering For all indices apart from convex hull, we varied the radius of the area that defined neighbours. We tested perceived numerosity using a fixed number of elements placed at random within a circular region. Observers compared two patterns (presented in two intervals) and chose the one that appeared more numerous. The same observers performed two other separate tasks in which they judged which pattern appeared more dispersed or more clustered. In each pair of images, the number was always the same (22, 28, 34, or 40 items), because we were interested in which "appeared" more numerous on the basis of spatial configuration. The results suggest that estimates of numerosity, dispersion, and clustering are based on different spatial information, that there are alternative approaches to quantifying clustering, and that in all cases clustering is linked to a decrease in perceived numerosity. The alternative measures have different properties and different practical and computational advantages.</p

Left ventricular diastolic dysfunction in normotensive postmenopausal women with type 2 diabetes mellitus.

Background The prevalence of heart failure among diabetic patients is high, also in those with normal blood pressure and without coronary artery disease, even when electrocardiogram (ECG) is normal. The goal of our study was to assess the prevalence of left ventricular diastolic dysfunction (LVDD) among diabetic women (DW) and its correlation with glycosylated hemoglobin (HbA1c) levels, obesity status, and ECG parameters. Methods : A group of 456 consecutive normotensive postmenopausal women affected by type 2 diabetes, diagnosed over 5 years, were enrolled. One hundred normotensive non-diabetic postmenopausal women were included as a control group (CG). Rest ECG and trans-thoracic echocardiogram and Doppler were performed. Results : LVDD was present in 103 (23.3%) out of 456 DW, and 8 out of 100 women in CG (8%), p &lt; 0.001. There was no difference in mean age between the two groups: 56 ± 13 and 55 ± 3, respectively (p = 0.3). There were 191 (41.9 %) DW with body mass index (BMI) &gt; 30 kg/m2. Among those, there were 56 (12.3%) with significant prevalence of LVDD, while there were 49 (10.7%) with BMI &lt; 30 kg/m2, p &lt; 0.005. DW with HbA1c &gt; 7.5% comprised a group of 243 (53.3%) patients. Among those, there were 45 (9.9%) with higher prevalence of LVDD, and 15 (3.3%) with HbA1c &lt; 7.5%), p &lt; 0.01. Out of a group of 147 (32.2%) DW with abnormal ECG , 21 had LVDD (4.6%), p = 0,1, and 84 (18.8%) had LVDD with normal ECG. Conclusions: Our data prove a high prevalence of LVDD in asymptomatic diabetic postmenopausal women. This finding is closely related with HbA1c levels and obesity status, not with abnormal ECG, which is a unique cardiologic test recommended by current guidelines in all diabetic patients. We conclude that early detection of high level of HbA1c and obesity (30 kg/m2) may identify women with major risk to develop LVDD. Furthermore, a simple ECG, when normal, is not enough to assess a normal LV diastolic function

Left ventricular diastolic dysfunction in normotensive postmenopausal women with type 2 diabetes mellitus

Background: The prevalence of heart failure among diabetic patients is high, also in those with normal blood pressure and without coronary artery disease, even when electrocardiogram (ECG) is normal. The goal of our study was to assess the prevalence of left ventricular diastolic dysfunction (LVDD) among diabetic women (DW) and its correlation with glycosylated hemoglobin (HbA1c) levels, obesity status, and ECG parameters. Methods: A group of 456 consecutive normotensive postmenopausal women affected by type 2 diabetes, diagnosed over 5 years, were enrolled. One hundred normotensive non-diabetic postmenopausal women were included as a control group (CG). Rest ECG and trans-thoracic echocardiogram and Doppler were performed. Results: LVDD was present in 103 (23.3%) out of 456 DW, and 8 out of 100 women in CG (8%), p &lt; 0.001. There was no difference in mean age between the two groups: 56 ± 13 and 55 ± 3, respectively (p = 0.3). There were 191 (41.9%) DW with body mass index (BMI) &gt; 30 kg/m2. Among those, there were 56 (12.3%) with significant prevalence of LVDD, while there were 49 (10.7%) with BMI &lt; 30 kg/m2, p &lt; 0.005. DW with HbA1c &gt; 7.5% comprised a group of 243 (53.3%) patients. Among those, there were 45 (9.9%) with higher prevalence of LVDD, and 15 (3.3%) with HbA1c &lt; 7.5%, p &lt; 0.01. Out of a group of 147 (32.2%) DW with abnormal ECG , 21 had LVDD (4.6%), p = 0,1, and 84 (18.8%) had LVDD with normal ECG. Conclusions: Our data prove a high prevalence of LVDD in asymptomatic diabetic postmenopausal women. This finding is closely related with HbA1c levels and obesity status, not with abnormal ECG, which is a unique cardiologic test recommended by current guidelines in all diabetic patients. We conclude that early detection of high level of HbA1c and obesity (30 kg/m2) may identify women with major risk to develop LVDD. Furthermore, a simple ECG, when normal, is not enough to assess a normal LV diastolic function.

The Effect of Graph Layout on the Perception of Graph Density: An Empirical Study

The visual representation of a graph is crucial in understanding and analyzing its properties. In this empirical study, we examine the effect of different drawing layouts on our perception of graph density. We treat density as an absolute property of the graph and use a Yes-No design, where participants have to decide whether a graph has a given density or not. We compare a simple grid layout with well-known planar and spring layouts. We also introduce an alternative ‘improved’ grid layout, which reduces the number of crossings while keeping most of the simplicity of the original grid layout. Results show that our ‘improved’ version of the grid layout facilitated performance on the task, compared to the original one. Moreover, participants were biased into judging graphs as denser when drawn with the original grid layout, while tended to perceive graphs as less dense when drawn with the planar and grid layouts. In contrast to previous studies on graph density perception, this is the first indication that the chosen layout can influence our perception of the graph’s density