A mechatronic approach to supernormal auditory localisation

Remote audio perception is a fundamental requirement for telepresence and teleoperation in applications that range from work in hostile environments to security and entertainment. The following paper presents the use of a mechatronic system to test the efficacy of audio for telepresence. It describes work to determine whether the use of supernormal inter-aural distance is a valid means of approaching an enhanced method of hearing for telepresence. The particular audio variable investigated is the azimuth angle of error and the construction of a dedicated mechatronic test rig is reported and the results obtained. The paper concludes by observing that the combination of the mechatronic system and supernormal audition does enhance the ability to localise sound sources and that further work in this area is justified

Microspectroscopy and Imaging in the THz Range Using Coherent CW Radiation

A novel THz near-field spectrometer is presented which allows to perform biological and medical studies with high spectral resolution combined with a spatial resolution down to l/100. In the setup an aperture much smaller than the used wavelength is placed in the beam very close to the sample. The sample is probed by the evanescent wave behind the aperture. The distance is measured extremely accurate by a confocal microscope. We use monochromatic sources which provide powerful coherent cw radiation tuneable from 50 GHz up to 1.5 THz. Transmission and reflection experiments can be performed which enable us to study solids and molecules in aqueous solution. Examples for spectroscopic investigations on biological tissues are presented.Comment: 4 pages, 5 figures, email: [email protected]

Detecting Recent Dynamics in Large-Scale Landslides via the Digital Image Correlation of Airborne Optic and LiDAR Datasets: Test Sites in South Tyrol (Italy)

Large-scale slow-moving deep-seated landslides are complex and potentially highly damaging phenomena. The detection of their dynamics in terms of displacement rate distribution is therefore a key point to achieve a better understanding of their behavior and support risk management. Due to their large dimensions, ranging from 1.5 to almost 4 km(2), in situ monitoring is generally integrated using satellite and airborne remote sensing techniques. In the framework of the EFRE-FESR SoLoMon project, three test-sites located in the Autonomous Province of Bolzano (Italy) were selected for testing the possibility of retrieving significant slope displacement data from the analysis of multi-temporal airborne optic and light detection and ranging (LiDAR) surveys with digital image correlation (DIC) algorithms such as normalized cross-correlation (NCC) and phase correlation (PC). The test-sites were selected for a number of reasons: they are relevant in terms of hazard and risk; they are representative of different type of slope movements (earth-slides, deep seated gravitational slope Deformation and rockslides), and different rates of displacement (from few cm/years to some m/years); and they have been mapped and monitored with ground-based systems for many years (DIC results can be validated both qualitatively and quantitatively). Specifically, NCC and PC algorithms were applied to high-resolution (5 to 25 cm/px) airborne optic and LiDAR-derived datasets (such as hillshade and slope maps computed from digital terrain models) acquired during the 2019-2021 period. Qualitative and quantitative validation was performed based on periodic GNSS surveys as well as on manual homologous point tracking. The displacement maps highlight that both DIC algorithms succeed in identifying and quantifying slope movements of multi-pixel magnitude in non-densely vegetated areas, while they struggle to quantify displacement patterns in areas characterized by movements of sub-pixel magnitude, especially if densely vegetated. Nonetheless, in all three landslides, they proved to be able to differentiate stable and active parts at the slope scale, thus representing a useful integration of punctual ground-based monitoring systems

Maximum-likelihood absorption tomography

Maximum-likelihood methods are applied to the problem of absorption tomography. The reconstruction is done with the help of an iterative algorithm. We show how the statistics of the illuminating beam can be incorporated into the reconstruction. The proposed reconstruction method can be considered as a useful alternative in the extreme cases where the standard ill-posed direct-inversion methods fail.Comment: 7 pages, 5 figure

Mediodorsal Thalamic Neurons Mirror the Activity of Medial Prefrontal Neurons Responding to Movement and Reinforcement during a Dynamic DNMTP Task

The mediodorsal nucleus (MD) interacts with medial prefrontal cortex (mPFC) to support learning and adaptive decision-making. MD receives driver (layer 5) and modulatory (layer 6) projections from PFC and is the main source of driver thalamic projections to middle cortical layers of PFC. Little is known about the activity of MD neurons and their influence on PFC during decision-making. We recorded MD neurons in rats performing a dynamic delayed nonmatching to position (dDNMTP) task and compared results to a previous study of mPFC with the same task (Onos et al., 2016). Criterion event-related responses were observed for 22% (254/1179) of neurons recorded in MD, 237 (93%) of which exhibited activity consistent with mPFC response types. More MD than mPFC neurons exhibited responses related to movement (45% vs. 29%) and reinforcement (51% vs. 27%). MD had few responses related to lever presses, and none related to preparation or memory delay, which constituted 43% of event-related activity in mPFC. Comparison of averaged normalized population activity and population response times confirmed the broad similarity of common response types in MD and mPFC and revealed differences in the onset and offset of some response types. Our results show that MD represents information about actions and outcomes essential for decision-making during dDNMTP, consistent with evidence from lesion studies that MD supports reward-based learning and action-selection. These findings support the hypothesis that MD reinforces task-relevant neural activity in PFC that gives rise to adaptive behavior

A Comparison of Cranial Cavity Extraction Tools for Non-contrast Enhanced CT Scans in Acute Stroke Patients

Cranial cavity extraction is often the first step in quantitative neuroimaging analyses. However, few automated, validated extraction tools have been developed for non-contrast enhanced CT scans (NECT). The purpose of this study was to compare and contrast freely available tools in an unseen dataset of real-world clinical NECT head scans in order to assess the performance and generalisability of these tools. This study included data from a demographically representative sample of 428 patients who had completed NECT scans following hospitalisation for stroke. In a subset of the scans (n = 20), the intracranial spaces were segmented using automated tools and compared to the gold standard of manual delineation to calculate accuracy, precision, recall, and dice similarity coefficient (DSC) values. Further, three readers independently performed regional visual comparisons of the quality of the results in a larger dataset (n = 428). Three tools were found; one of these had unreliable performance so subsequent evaluation was discontinued. The remaining tools included one that was adapted from the FMRIB software library (fBET) and a convolutional neural network- based tool (rBET). Quantitative comparison showed comparable accuracy, precision, recall and DSC values (fBET: 0.984 ± 0.002; rBET: 0.984 ± 0.003; p = 0.99) between the tools; however, intracranial volume was overestimated. Visual comparisons identified characteristic regional differences in the resulting cranial cavity segmentations. Overall fBET had highest visual quality ratings and was preferred by the readers in the majority of subject results (84%). However, both tools produced high quality extractions of the intracranial space and our findings should improve confidence in these automated CT tools. Pre- and post-processing techniques may further improve these results. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12021-021-09534-7

A Hybrid Learning Space for Physically-Active Mathematics: the case of Numberfit

This paper presents the case study of an intervention called Numberfit that aims at capturing primarily students’ interest in mathematics by combining team games and physical activity. We describe the hybrid learning space that is created through this approach that includes an online platform that allows the teachers and facilitators to design a lesson plan, input student scores and visualise a leaderboard. At the same time, various digital and tangible resources engage students in group (collaborative or competitive) activities while practising a range of topics in mathematics. We examine the changing role of the teacher and provide some methodological insights for conducting research in relation to student’s affect, motivation and behaviour in this context

Adaptive estimation in circular functional linear models

We consider the problem of estimating the slope parameter in circular functional linear regression, where scalar responses Y1,...,Yn are modeled in dependence of 1-periodic, second order stationary random functions X1,...,Xn. We consider an orthogonal series estimator of the slope function, by replacing the first m theoretical coefficients of its development in the trigonometric basis by adequate estimators. Wepropose a model selection procedure for m in a set of admissible values, by defining a contrast function minimized by our estimator and a theoretical penalty function; this first step assumes the degree of ill posedness to be known. Then we generalize the procedure to a random set of admissible m's and a random penalty function. The resulting estimator is completely data driven and reaches automatically what is known to be the optimal minimax rate of convergence, in term of a general weighted L2-risk. This means that we provide adaptive estimators of both the slope function and its derivatives