Miracle-dz/UniFAR-FTUs huggingface dataset:
ERD modulations during motor imageries relate to users' traits and BCI performances

Improving user performances is one of the major issues for Motor Imagery (MI) - based BCI control. MI-BCIs exploit the modulation of sensorimotor rhythms (SMR) over the motor and sensorimotor cortices to discriminate several mental states and enable user interaction. Such modulations are known as Event-Related Desynchronization (ERD) and Synchronization (ERS), coming from the mu (7–13 Hz) and beta (15–30 Hz) frequency bands. This kind of BCI opens up promising fields, particularly to control assistive technologies, for sport training or even for post-stroke motor rehabilitation. However, MI - BCIs remain barely used outside laboratories, notably due to their lack of robustness and usability (15 to 30% of users seem unable to gain control of an MI-BCI). One way to increase user performance would be to better understand the relationships between user traits and ERD/ERS modulations underlying BCI performance. Therefore, in this article we analyzed how cerebral motor patterns underlying MI tasks (i.e., ERDs and ERSs) are modulated depending (i) on nature of the task (i.e., right-hand MI and left-hand MI), (ii) the session during which the task was performed (i.e., calibration or user training) and (iii) on the characteristics of the user (e.g., age, gender, manual activity, personality traits) on a large MI-BCI data base of $\mathrm{N}=75$ participants. One of the originality of this study is to combine the investigation of human factors related to the user's traits and the neurophysiological ERD modulations during the MI task. Our study revealed for the first time an association between ERD and self-control from the 16PF5 questionnaire.
Application of Mathematical Models of Induction Heating in a Dynamic Modeling Environment

In the aerospace industry, induction brazing in various environments is widely used, for example, low-temperature brazing, brazing in protective environments, but the classical method of connecting waveguide paths (pipe-flange-pipe) is not inferior to them. However, induction heating is a very complex research process, which continues to this day. In this work, the authors have proposed a mathematical apparatus based on thermal processes during verification. A stationary continuously operating source of heat is considered, considering the equalization of temperatures, since the rate of heating of the solder and flux at the junction of the pipeline and the flange has a huge influence on the formation of the penetration channel. Also, the authors propose the use of regression analysis to restore the values of technological parameters. Thus, the approach used will allow considering the change in thermophysical parameters in the process of induction heating, which will increase the accuracy of the results obtained. The Russian development SimInTech is used as a modeling tool. The application of the approach proposed in the article can significantly reduce labor and material costs for the development of the technological process of induction soldering of waveguide paths both when correcting existing processes and when introducing new standard sizes and materials of products into production.
nnUNet-based Multi-modality Breast MRI Segmentation and Tissue-Delineating Phantom for Robotic Tumor Surgery Planning

Segmentation of the thoracic region and breast tissues is crucial for analyzing and diagnosing the presence of breast masses. This paper introduces a medical image segmentation architecture that aggregates two neural networks based on the state-of-the-art nnU-Net. Additionally, this study proposes a polyvinyl alcohol cryogel (PVA-C) breast phantom, based on its automated segmentation approach, to enable planning and navigation experiments for robotic breast surgery. The dataset consists of multimodality breast MRI of T2W and STIR images obtained from 10 patients. A statistical analysis of segmentation tasks emphasizes the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. We first use a single class labeling to segment the breast region and then exploit it as an input for three-class labeling to segment fatty, fibroglandular (FGT), and tumorous tissues. The first network has a 0.95 DCS, while the second network has a 0.95, 0.83, and 0.41 for fat, FGT, and tumor classes, respectively. Clinical Relevance—This research is relevant to the breast surgery community as it establishes a deep learning-based (DL) algorithmic and phantomic foundation for surgical planning and navigation that will exploit preoperative multimodal MRI and intraoperative ultrasound to achieve highly cosmetic breast surgery. In addition, the planning and navigation will guide a robot that can cut, resect, bag, and grasp a tissue mass that encapsulates breast tumors and positive tissue margins. This image-guided robotic approach promises to potentiate the accuracy of breast surgeons and improve patient outcomes.
Evaluation of Data Driven Respiratory Signal Extraction Methods from Cone-Beam CT using MR-based Digital Phantoms

A comparative study to assess the performance of data-driven methods to estimate respiratory signals from Cone-Beam CT (CBCT) projection data was earlier implemented using a recently developed 4D MRI-based CBCT digital phantom dataset. However, a more extensive study is deemed necessary to assess the accuracy and robustness of each method rather than just rely on a single dataset. Thus, the purpose of this work is to further investigate the methods using the developed 4D MRI-based CBCT digital phantom datasets from five volunteers with actual different anatomical and breathing features. The effect of a wider range of justified projection region-of-interest (ROI) selection is also considered to further assess these methods. The results indicate that the Local Principal Component Analysis (LPCA) estimates respiration most accurately with an overall mean local correlation coefficient value of 0.875 compared to the Amsterdam Shroud (AS), Intensity Analysis (IA) and Fourier Transform (FT) methods. The LPCA method also demonstrated robustness with the highest mean local correlation across the different ROIs as long as the diaphragm is present. This is dissimilar to the high sensitivity of the FT method on the visibility of the diaphragm regardless of the presence of other organs.
Direct-gap photoluminescence with tunable emission wavelength in Ge1−ySny alloys on silicon

Direct-gap photoluminescence has been observed at room temperature in Ge1−ySny alloys grown on (001) Si substrates. The emission wavelength is tunable over a 90 meV (200 nm) range by increasing the Sn concentration from y=0 to y=0.03. A weaker feature at lower energy is assigned to the indirect gap transitions, and the separation between the direct and indirect emission peaks is found to decrease as a function of y, as expected for these alloys. These results suggest that Ge1−ySny alloys represent an attractive alternative to Ge for the fabrication of laser devices on Si.
Selection of Optimal Physiological Features for Accurate Detection of Stress

Stress is an established risk factor in the development of addiction and in reinstating drug seeking. Substance use disorder (SUD) is a dangerous epidemic that affects the brain and behavior. Despite this growing epidemic and its subsequent consequences, there are limited management and treatment options, pharmacotherapies and psychosocial treatments available. To this end, there is a need for new and improved personalized devices and treatments for the detection and management of SUD. Based on documented negative effects of stress in SUD, in this paper, our objective was to select a few significant physiological features from a set of 8 features collected by a chest-worn RespiBAN Professional in 15 individuals. We used three machine learning classifiers on these optimal physiological features to detect stress. Our results indicate that best accuracies were achieved when electrodermal activity (EDA), body temperature and chest-worn accelerometer were considered as features for the classification. Challenges, implications and applications were discussed. In the near future, the proposed methods will be replicated in individuals with SUD.
Investigation of crimping effects on the stent deployment through in silico modeling

Atherosclerosis is one of the most mortal diseases that affects the arterial vessels, due to accumulation of plaque, altering the hemodynamic environment of the artery by preventing the sufficient delivery of blood to other organs. Stents are expandable tubular wires, used as a treatment option. In silico studies have been extensively exploited towards examining the performance of such devices by employing Finite Element Modeling. This study models the crimping stage during stent implantation to examine the effect of inclusion of pre-stress state of the stent. The results show that modeling of the crimping stress state of the stent prior to the deployment results in under-expansion of the stent, due to the indirect inclusion of strain-induced hardening effects. As a result, it is evident that the compressive stent stress configuration is important to be considered in the computational modeling approaches of stent deployment.
Computational Evaluation of Combined Cerebellar and Frontal Transcranial Direct Current Stimulation for Treatment-Resistant Depression

This work aimed to estimate the distribution of the electric field generated by a combined cerebellar and frontal transcranial direct current stimulation (tDCS) for treatment-resistant depression using electromagnetics computational techniques applied to a realistic head human model. Results showed that the stronger electric fields occur mainly in the cerebellum and in DLPFC areas, where the two pairs of electrodes were applied. Furthermore, the study demonstrated that the simultaneous use of the two pairs of electrodes did not imply a lower effectiveness of the tDCS technique, in fact the electric field distributions in the primarily targets of the anatomical regions (i.e., cerebellum and DLPFC) were very similar to when the pairs of electrodes were applied separately.
Cluster Observations with the South Pole Telescope

The South Pole Telescope (SPT) is an instrument designed to survey galaxy clusters using the Sunyaev-Zel'dovich effect. In this thesis I describe the telescope, its first-generation receiver, and its readout and control systems. I also present Sunyaev-Zel'dovich measurements of 15 massive X-ray selected galaxy clusters obtained with the SPT. The cluster signals are measured at 150 GHz, and concurrent 220 GHz data are used to reduce astrophysical contamination. Radial profiles are computed using a technique that takes into account the effects of the beams and filtering. In several clusters, significant SZ decrements are detected out to a substantial fraction of the virial radius. The profiles are fit to the -model and to a generalized NFW pressure profile, and are scaled and stacked to probe their average behavior. The best-fit model parameters are consistent with previous studies: b = 0.86 and rcore/r500 = 0.20 for the b-model, and (a, b, g, c500)=(1.0, 5.5, 0.5, 1.0) for the generalized NFW model. Both models fit the SPT data comparably well, and both are consistent with the average SZ profile out to the virial radius. The integrated Compton-y parameter YSZ is computed for each cluster using both model-dependent and model-independent techniques, and the results are compared to X-ray estimates of cluster parameters. YSZ is found to scale with YX and gas mass with low scatter. Since these observables have been found to scale with total mass, these results point to a tight mass-observable relation for the SPT cluster survey.
Exploration of the Severity of Hepatic Encephalopathy Deterioration Process Through Dynamics of the EEG Band Power time series

Cirrhosis is a liver disease that could impair the functionality of the neural system, limiting cognitive processes and reduce mobility, possibly because alterations on dynamics of neural communications. In this study the dynamics of band power fluctuations on ß and γ bands were studied on three groups: control, patients with cirrhosis and patients diagnosed with minimal hepatic encephalopathy. EEG signals were recorded on an oddball paradigm and analyzed to obtain the latency and amplitude of the P300 wave and Detrended Fluctuation Analysis (DFA) exponent values over ß and γ band power time series for each channel. As expected, the latency of the P300 wave was significantly different for control subjects ($p< 10^{-12}$). Amplitudes were not as different, but they tend to decrease for cirrhosis and minimal hepatic encephalopathy groups. DFA exponent values also tends to describe a more regular process and seems to be related with presence of symptoms of hepatic encephalopathy. These subtle changes could be produced by the mechanisms of a mild neurological impairment and be used, if it is confirmed, as an index to evaluate such deterioration.
Language-Independent Sleepy Speech Detection

Prolonged sleepiness can lead to impairment of cognitive and physical performance and may cause unfortunate accidents. Speech signals are easily accessible using a simple microphone or other means, hence, automated approaches for accurate sleepiness detection from speech signals are desired to prevent degradation in human performance and accidental injury. Sleepiness is known to affect acoustic patterns of speech so that they are different from those of normal speech, and this change is also independent of the language being spoken. To date, there have been no studies examining linguistic-independent sleepy speech detection. We used two different languages, English and German, to detect sleepy speech, where the former was used to train/validate and the latter to test the effectiveness of machine and deep learning models. Specifically, we trained ResNet50, a deep learning model, and five machine learning models with relevant vocal features. Speech data segments from three English-speaking subjects were used for training the model and segments from an English-speaking subject were used for validation. We then tested ResNet50 and the five different machine-learning models using speech data segments from one German-speaking subject. Deep learning far outperformed all of the machine learning approaches. The accuracy, sensitivity, specificity, and geometric mean values were found to be 0.96, 0.92, 0.99, and 0.95, respectively, using ResNet50 on the test data. Our preliminary results suggest that sleepiness can be accurately detected independently from linguistic speech. Clinical Relevance—It is not known if sleepiness can be detected regardless of the language spoken. Our results show the feasibility of accurate sleepiness detection using deep learning even when tested with a different language than trained on
A Bounding-Box Regression Model for Colorectal Tumor Detection in CT Images Via Two Contrary Networks

The field of medical image analysis has been attracted to deep learning. Various deep learning-based techniques have been introduced to aid diagnosis in the CT image of the patient. The auxiliary model for diagnosis that we proposed is to detect colorectal tumors in the CT image. The model is combined with two contrary networks of 'Detection Transformer” and 'Hourglass”. Furthermore. , to improve the performance of the model. , we propose an efficient connection method for two contrary models by using intermediate prediction information. A total of 3. ,509 patients (193.,567 CT images) were applied to the experiment and our model outperforms the conventional models in colorectal tumor detection. Clinical Relevance - The proposed model in this paper automatically detects colorectal tumors and provides the bounding box in the CT images. Colorectal tumor is one of the common diseases. In addition, the mortality rate is so high that in-time treatment is required. The model we present here has a sensitivity (or recall) of 84.73 % for tumor detection and a precision of 88.25 % in the patient CT data. The in-slice performance of the tumor detection shows an IoU of 0.56, a sensitivity of 0.67, and a precision of 0.68.
Systematic Evaluation of the Impact of Involuntary Motion in Whole Body Dynamic PET

Involuntary patient motion can happen in dynamic whole body (DWB) PET due to long scanning times, which may cause inaccurate quantification of tissue parameters. To quantify the impact on Patlak parameters, we simulated dynamic data using patient-derived motion fields, systematically introducing the motion at different passes of the dynamic scan, both inter and intra-frame. Estimated parameters are compared against the ground truth. Results show that errors can be large, even for small motion. Caution is advised when quantitatively evaluating DWB-PET images, if any motion has been detected.
A Low-Profile, Risley-Prism-Based, Beam-Steerable Antenna Employing a Single Flat Prism

We present a low-profile, mechanically beamsteerable antenna that implements the Risley prism beamsteering concept using only a single flat prism. The proposed antenna achieves 2D beam steering and consists of two parts: a holographic leaky-wave antenna and a phase-shifting surface (PSS) that acts as a flat prism. The lower holographic leakywave antenna both acts as the feed for the prism and provides a phase-shifting gradient over its output aperture. When paired with the flat prism, the system can provide 2D beam steering by mechanically rotating the two layers against each other. Unlike conventional Risley-prism-based beam-steerable antennas, the proposed approach results in a very low-profile antenna and does not need spatial illumination of a two-prism system with a separate feed antenna. The flat prism is implemented using a PSS consisting of low-pass, hexagonal-shaped, spatial phase shifters. A prototype antenna with an overall thickness of only 1.1 λ0 at 10 GHz was designed, fabricated, and experimentally characterized. Measurement results agree well with theoretical predictions and both show wide-angle beam scanning ranging from 0°-57° in the elevation and 0°-360° in the azimuth plane. The proposed antenna system has an equivalent aperture diameter of 7.1 λ0 and a measured peak gain of 22.3 dBc at 10 GHz, corresponding to an aperture efficiency of 34.5%. The proposed concept is expected to be useful in designing low-profile, beam-steerable antennas in which scanning speed can be traded off to reduce system complexity.
On the way for the best imaging features from CT images to predict EGFR Mutation Status in Lung Cancer

Artificial Intelligence-based tools have shown promising results to help clinicians in diagnosis tasks. Radio-genomics would aid in the genotype characterization using information from radiologic images. The prediction of the mutations status of main oncogenes associated with lung cancer will help the clinicians to have a more accurate diagnosis and a personalized treatment plan, decreasing the need to use the biopsy. In this work, novel and objective features were extracted from the lung that contained the nodule, and several machine learning methods were combined with feature selection techniques to select the best approach to predict the EGFR mutation status in lung cancer CT images. An AUC of 0.756 ± 0.055 was obtained using a logistic regression and independent component analysis as feature selector, supporting the hypothesis that CT images can capture pathophysiological information with great value for clinical assessment and personalized medicine of lung cancer. Clinical Relevance — Radiogenomic approaches could be an interesting help for lung cancer characterization. This work represents a preliminary study for the development of computer-aided decision systems to provide a more accurate and fast characterization of lung cancer which is fundamental for an adequate treatment plan for lung cancer patients.
Improved PET Tomographic Image Reconstruction by Employing the RISE Method

Improved tomographic images are obtained via the Reconstructed Image from Simulations Ensemble (RISE) method in Positron Emission Tomography (PET) using a clinical FDG brain dataset. We compare the RISE results with those provided by the scanner’s reconstruction software, an OSEM with PSF correction algorithm. The improvememnt is quantified by employing the Contrast-to-Noise ratio (CNR) and Full-Width Half Maximum (FWHM). Both visually and quantitatively, RISE exhibits higher CNR and lower FWHM compared to the OSEM-PSF reconstruction. These results are in accord with the noticable improvement which was observed when RISE was implemented in SPECT image reconstructions.
Dual-Band Closed-Slot MIMO Antenna for Terminal Wireless Applications

This paper presents a dual-band closed-slot multi-ple-input multiple-output (MIMO) antenna for terminal wireless applications. By simply inserting additional slots between the closely-spaced parallel-arranged radiation slots, the signal im-pedances of common mode (CM) and differential mode (DM) can achieve a similar status, the strong mutual coupling effect is then eliminated. The proposed slot antenna pair shows good isolation of better than 20 dB across the desired WIFI bands. In addition to the one-wavelength slot antenna, flexible extension to a more compact half-wavelength slot pair is given for future industrial design (ID), which provides further validation of the method. We fabricate the 4×4 MIMO antenna system constituted by two sets of one-wavelength closed-slot pairs. The experimental results show that the proposed MIMO antenna can offer an isolation of better than 18 dB, an envelope correlation coefficient (ECC) of lower than 0.1 between all ports, and a wide -3 dB efficiency bandwidths across the desired bands. The results confirm that the proposed antenna is a viable candidate for WIFI communica-tions inside routers.
A Comparison of Video-based Methods for Neonatal Body Motion Detection

Preterm infants in a neonatal intensive care unit (NICU) are continuously monitored for their vital signs, such as heart rate and oxygen saturation. Body motion patterns are documented intermittently by clinical observations. Changing motion patterns in preterm infants are associated with maturation and clinical events such as late-onset sepsis and seizures. However, continuous motion monitoring in the NICU setting is not yet performed. Video-based motion monitoring is a promising method due to its non-contact nature and therefore unobtrusiveness. This study aims to determine the feasibility of simple video-based methods for infant body motion detection. We investigated and compared four methods to detect the motion in videos of infants, using two datasets acquired with different types of cameras. The thermal dataset contains 32 hours of annotated videos from 13 infants in open beds. The RGB dataset contains 9 hours of annotated videos from 5 infants in incubators. The compared methods include background substruction (BS), sparse optical flow (SOF), dense optical flow (DOF), and oriented FAST and rotated BRIEF (ORB). The detection performance and computation time were evaluated by the area under receiver operating curves (AUC) and run time. We conducted experiments to detect motion and gross motion respectively. In the thermal dataset, the best performance of both experiments is achieved by BS with mean (standard deviation) AUCs of 0.86 (0.03) and 0.93 (0.03). In the RGB dataset, SOF outperforms the other methods in both experiments with AUCs of 0.82 (0.10) and 0.91 (0.05). All methods are efficient to be integrated into a camera system when using low-resolution thermal cameras.