Modelling asymmetry in the peripheral auditory system of the lizard

The ears of lizards are highly directional. The directionality is generated by strong acoustical coupling of the eardrums. A simple lumped-parameter model of the ear followed by binaural comparisons has been shown to perform successful phonotaxis in robot implementations. However, such a model will produce localization errors in the form of response bias if the ears are asymmetrical. We have evaluated how large errors are generated by asymmetry using simulations of the ear model in Mathematica 5.2. The study shows that the effect of asymmetry is minimal around the most directional frequency of the ear, but that biases reduce the useful bandwidth of localization. This work was presented in part at the First European Workshop on Artificial Life and Robotics, Vienna, Austria, July 12–13, 2007     

Creating geometrically robust designs for highly sensitive problems using topology optimization

Resonance and wave-propagation problems are known to be highly sensitive towards parameter variations. This paper discusses topology optimization formulations for creating designs that perform robustly under spatial variations for acoustic cavity problems. For several structural problems, robust topology optimization methods have already proven their worth. However, it is shown that direct application of such methods is not suitable for the acoustic problem under consideration. A new double filter approach is suggested which makes robust optimization for spatial variations possible. Its effect and limitations are discussed. In addition, a known explicit penalization approach is considered for comparison. For near-uniform spatial variations it is shown that highly robust designs can be obtained using the double filter approach. It is finally demonstrated that taking non-uniform variations into account further improves the robustness of the designs.     

Learning nonlinear image manifolds by global alignment of local linear models

Appearance-based methods, based on statistical models of the pixel values in an image (region) rather than geometrical object models, are increasingly popular in computer vision. In many applications, the number of degrees of freedom (DOF) in the image generating process is much lower than the number of pixels in the image. If there is a smooth function that maps the DOF to the pixel values, then the images are confined to a low-dimensional manifold embedded in the image space. We propose a method based on probabilistic mixtures of factor analyzers to 1) model the density of images sampled from such manifolds and 2) recover global parameterizations of the manifold. A globally nonlinear probabilistic two-way mapping between coordinates on the manifold and images is obtained by combining several, locally valid, linear mappings. We propose a parameter estimation scheme that improves upon an existing scheme and experimentally compare the presented approach to self-organizing maps, generative topographic mapping, and mixtures of factor analyzers. In addition, we show that the approach also applies to finding mappings between different embeddings of the same manifold.     

Long COVID: Association of Functional Autoantibodies against G-Protein-Coupled Receptors with an Impaired Retinal Microcirculation

Long COVID (LC) describes the clinical phenotype of symptoms after infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Diagnostic and therapeutic options are limited, as the pathomechanism of LC is elusive. As the number of acute SARS-CoV-2 infections was and is large, LC will be a challenge for the healthcare system. Previous studies revealed an impaired blood flow, the formation of microclots, and autoimmune mechanisms as potential factors in this complex interplay. Since functionally active autoantibodies against G-protein-coupled receptors (GPCR-AAbs) were observed in patients after SARS-CoV-2 infection, this study aimed to correlate the appearance of GPCR-AAbs with capillary microcirculation. The seropositivity of GPCR-AAbs was measured by an established cardiomyocyte bioassay in 42 patients with LC and 6 controls. Retinal microcirculation was measured by OCT–angiography and quantified as macula and peripapillary vessel density (VD) by the Erlangen-Angio Tool. A statistical analysis yielded impaired VD in patients with LC compared to the controls, which was accentuated in female persons. A significant decrease in macula and peripapillary VD for AAbs targeting adrenergic β2-receptor, MAS-receptor angiotensin-II-type-1 receptor, and adrenergic α1-receptor were observed. The present study might suggest that a seropositivity of GPCR-AAbs can be linked to an impaired retinal capillary microcirculation, potentially mirroring the systemic microcirculation with consecutive clinical symptoms.     

A transmitter IC for TETRA systems based on a Cartesian feedback loop linearization technique

A fully integrated linearized transmitter for the next generation TETRA systems for PMR and public safety applications in a CMOS-based SiGe technology is described. The presented single-chip transmitter employs a Cartesian feedback loop technique in order to improve the linearity of the externally connected power amplifier. The transmitter is usable in a frequency range from 300 MHz up to 800 MHz, providing a linearity improvement of more than 40 dB.     

No-reference analysis of decoded MPEG images for PSNR estimation and post-processing

We propose no-reference analysis and processing of DCT (Discrete Cosine Transform) coded images based on estimation of selected MPEG parameters from the decoded video. The goal is to assess MPEG video quality and perform post-processing without access to neither the original stream nor the code stream. Solutions are presented for MPEG-2 video. A method to estimate the quantization parameters of DCT coded images and MPEG I-frames at the macro-block level is presented. The results of this analysis is used for deblocking and deringing artifact reduction and no-reference PSNR estimation without code stream access. An adaptive deringing method using texture classification is presented. On the test set, the quantization parameters in MPEG-2 I-frames are estimated with an overall accuracy of 99.9% and the PSNR is estimated with an overall average error of 0.3 dB. The deringing and deblocking algorithms yield improvements of 0.3 dB on the MPEG-2 decoded test sequences.     

How to improve intrinsic and extrinsic reliability of vias by process optimization

A systematic study of various processes and their impact on intrinsic reliability has been performed on Cu dual damascene interconnects. The most significant improvement for intrinsic reliability is the ‘break-through’ liner. A strong impact on stressmigration (SM) was revealed using a HDP based SiN deposition on top of Cu lines. Early failures in electromigration (EM) studies are present with insufficient cleaning processes. No reliability impact was detected with different plating and slurry chemistries and liner thickness increase. Extrinsic via reliability is assessed with a special test chip comprising 3E9 via/wafer. High ohmic vias are identified before and after thermal stress. As an example, the failure rates in Cu dual damascene levels with relaxed pitch before and after cleaning optimization are discussed.     

Automated effect-specific mammographic pattern measures

We investigate the possibility to develop methodologies for assessing effect specific structural changes of the breast tissue using a general statistical machine learning framework. We present an approach of obtaining objective mammographic pattern measures quantifying a specific biological effect, such as hormone replacement therapy (HRT). We compare results using this approach to using standard density measures. We show that the proposed method can quantify both age related effects and effects caused by HRT. Age effects are significantly detected by our method where standard methodologies fail. The separation of HRT subpopulations using our approach is comparable to the best methodology, which is interactive.      

AUTO-SMASH: A self-calibrating technique for SMASH imaging

Recently a new fast magnetic resonance imaging strategy, SMASH, has been described, which is based on partially parallel imaging with radiofrequency coil arrays. In this paper, an internal sensitivity calibration technique for the SMASH imaging method using self-calibration signals is described. Coil sensitivity information required for SMASH imaging is obtained during the actual scan using correlations between undersampled SMASH signal data and additionally sampled calibration signals with appropriate offsets ink-space. The advantages of this sensitivity reference method are that no extra coil array sensitivity maps have to be acquired and that it provides coil sensitivity information in areas of highly non-uniform spin-density. This auto-calibrating approach can be easily implemented with only a small sacrifice of the overall time savings afforded by SMASH imaging. The results obtained from phantom imaging experiments and from cardiac studies in nine volunteers indicate that the self-calibrating approach is an effective method to increase the potential and the flexibility of rapid imaging with SMASH.