Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear Features from Multi-Modal Signals for Continuous Stress Monitoring

Continuous monitoring of stress in individuals during their daily activities has become an inevitable need in present times. Unattended stress is a silent killer and may lead to fatal physical and mental disorders if left unidentified. Stress identification based on individual judgement often leads to under-diagnosis and delayed treatment possibilities. EEG-based stress monitoring is quite popular in this context, but impractical to use for continuous remote monitoring.Continuous remote monitoring of stress using signals acquired from everyday wearables like smart watches is the best alternative here. Non-EEG data such as heart rate and ectodermal activity can also act as indicators of physiological stress. In this work, we have explored the possibility of using nonlinear features from non-EEG data such as (a) heart rate, (b) ectodermal activity, (c) body temperature (d) SpO2 and (e) acceleration in detecting four different types of neurological states; namely (1) Relaxed state, (2) State of Physical stress, (3) State of Cognitive stress and (4) State of Emotional stress. Physiological data of 20 healthy adults have been used from the noneeg database of PhysioNet.Results: We used two machine learning models; a linear logistic regression and a nonlinear random forest to detect (a) stress from relaxed state and (4) the four different neurological states. We trained the models using linear and nonlinear features separately. For the 2-class and 4-class problems, using nonlinear features increased the accuracy of the models. Moreover, it is also proved in this study that by using nonlinear features, we can avoid the use of complex machine learning models.
Improving Equity in Deep Learning Medical Applications with the Gerchberg-Saxton Algorithm

Deep learning (DL) has gained prominence in healthcare for its ability to facilitate early diagnosis, treatment identification with associated prognosis, and varying patient outcome predictions. However, because of highly variable medical practices and unsystematic data collection approaches, DL can unfortunately exacerbate biases and distort estimates. For example, the presence of sampling bias poses a significant challenge to the efficacy and generalizability of any statistical model. Even with DL approaches selection bias can lead to inconsistent, suboptimal, or inaccurate model results, especially for underrepresented populations. Therefore, without addressing bias, wider implementation of DL approaches can potentially cause unintended harm. In this paper, we studied a novel method for bias reduction that leverages the frequency domain transformation via Gerchberg-Saxton and corresponding impact on the outcome from racio-ethnic bias perspective.
The Process of Primary Frequency Control of the Power Systems Investigation Using Modeling Methods

General primary frequency regulation (GPFR) is employed within the power system to limit frequency variation deviations from a reference value in positive and negative directions, ensuring safe power object operations and reducing the prevalence of power outages. Requirements for primary frequency regulation are presented in normative documents. They regulate the statism of regulation (from 4 to 6%) and the minimum time of increase/decrease of power plant capacity. However, in practice, undamped frequency fluctuations and unnecessary consumer and generation outages occur in frequency regulation in power systems in some emergency disturbances. In 2020, the Norilsk-Taimyr power system experienced an accident with an unbalanced active power of 40 MW. The frequency was regulated by two power plants - Ust-Khantai and Kurey HPPs. Low-frequency oscillations occurred for 27 seconds. Only with a statism of 25 percent, which does not match the standards, a stable frequency control could be achieved. This case highlights the problem of suboptimal GPFC laws in frequency control of multiple power plants. This paper presents the modelling of the power system in MATLAB environment to investigate the transients in GPFC. Based on the data obtained, the shortcomings of the control laws used can be noted and criteria for the creation of modernized control algorithms can be put forward.
TBENet:A two-branch boundary enhancement Network for cerebrovascular segmentation

Cerebrovascular segmentation in digital subtraction angiography (DSA) images is the gold standard for clinical diagnosis. However, owing to the complexity of cerebrovascular, automatic cerebrovascular segmentation in DSA images is a challenging task. In this paper, we propose a CNN-based Two-branch Boundary Enhancement Network (TBENet) for automatic segmentation of cerebrovascular in DSA images. The TBENet is inspired by U-Net and designed as an encoder-decoder architecture. We propose an additional boundary branch to segment the boundary of cerebrovascular and a Main and Boundary branches Fusion Module (MBFM) to integrate the boundary branch outcome with the main branch outcome to achieve better segmentation performance. The TBENet was evaluated on HMCDSA (an in-house DSA cerebrovascular dataset), and reaches 0.9611, 0.7486, 0.7152, 0.9860 and 0.9556 in Accuracy, F1 score, Sensitivity, Specificity, and AUC, respectively. Meanwhile, we tested our TBENet on the public vessel segmentation benchmark DRIVE, and the results show that our TBENet can be extended to diverse vessel segmentation tasks.
Weakly-Supervised Keypoint Detector for Fishing Ground Estimation

Fishing ground estimation is an important task in the fishing industry. In this study, we focus on fisher's decision-making process based on sea surface temperature patterns, which we regard as a pattern recognition problem. Our method involves treating fishing grounds as keypoints and employing a deep learning-based keypoint detector to estimate their locations from sea surface temperature maps. However, the available amount of catch data, which is essential for annotation purposes, is insufficient. To tackle this issue, we utilize fishing vessel trajectory data as weak supervision, despite its limited information, owing to its abundance. Through experimental results, we demonstrated the superior performance of our method compared to approaches relying solely on catch data.
Robust Point Cloud Registration with Geometry-based Transformation Invariant Descriptor

This work presents a novel method for point registration in 3D space. The proposed algorithm utilizes transformation-invariant geometry information to estimate the pose of objects based on correspondences between points in two sets. Conventional methods use geometry descriptors to find these correspondences, which can result in a large number of outliers. Most existing algorithms are error-prone when outliers are present. Instead of formulating point registration as a non-convex optimization problem, we propose an intuitive method that filters out spurious correspondences. This is achieved by evaluating three different geometry-based transformation-invariant descriptors for outlier removal. We construct fully connected graphs with the proposed descriptors on correspondences, and convert the outlier removal problem into a subgraph isomorphism problem that is solved using a binary clustering approach. The resulting inlier clustering is used to estimate the transformation between the two point sets. The effectiveness of the proposed approach is evaluated on standard 3D data and the 3DMatch scan matching dataset, and compared against existing state-of-the-art methods. Results show that our method effectively reduces outliers and performs similarly to these methods.
Flexible Broad-Range Graphene-based Piezoresistive Pressure Sensor

In recent years, there has been a considerable emphasis on the development of flexible wearable sensors as a means of enhancing quality of life through advanced technologies. One type of sensor that has garnered significant interest is the wearable flexible pressure sensor, which holds great potential for healthcare applications and monitoring human motions. In pursuit of this objective, two piezoresistive pressure sensors were created by employing Graphene nanoplates (GNPs) and polyvinylidene fluoride (PVDF) and coatings them on a commercial polyester substrate, each with different concentrations of GNPs in PVDF. Examination through scanning electron microscopy reveals a uniform coating morphology on the surface of the substrate. The sensor with a higher concentration of GNP exhibits greater sensitivity, with a value of 0.139 kP$\mathrm{a}^{-1}$ over a broad operating range of 0 to 90 kPa, and 0.114 kP$\mathrm{a}^{-1}$ within the pressure range of 90 to 200 kPa. Furthermore, the response and recovery times of the sensor are 2 ms and 4 ms, respectively. This study demonstrates a promising approach for the development of flexible wearable sensors that can be integrated into footwear to monitor plantar motion activities such as walking, jumping, and standing. As a result, this approach holds significant potential for a range of physiological activity applications.
Donut: Augmentation Technique for Enhancing The Efficacy of Glaucoma Suspect Screening

Glaucoma is the second most common cause of blindness. A glaucoma suspect has risk factors that increase the possibility of developing glaucoma. Evaluating a patient with suspected glaucoma is challenging. The "donut method" was developed in this study as an augmentation technique for obtaining high-quality fundus images for training ConvNeXt-Small model. Fundus images from GlauCUTU-DATA, labelled by randomizing at least 3 well-trained ophthalmologists (4 well-trained ophthalmologists in case of no majority agreement) with a unanimous agreement (3/3) and majority agreement (2/3), were used in the experiment. The experimental results from the proposed method showed the training model with the "donut method" increased the sensitivity of glaucoma suspects from 52.94% to 70.59% for the 3/3 data and increased the sensitivity of glaucoma suspects from 37.78% to 42.22% for the 2/3 data. This method enhanced the efficacy of classifying glaucoma suspects in both equalizing sensitivity and specificity sufficiently. Furthermore, three well-trained ophthalmologists agreed that the GradCAM++ heatmaps obtained from the training model using the proposed method highlighted the clinical criteria.Clinical relevance– The donut method for augmentation fundus images focuses on the optic nerve head region for enhancing efficacy of glaucoma suspect screening, and uses Grad-CAM++ to highlight the clinical criteria.
Identification of Seizure Onset Zone from Intracranial EEG Using Source Selection-Based Domain Adaptation

For focal epilepsy patients, correctly identifying the seizure onset zone (SOZ) is essential for surgical treatment. In automated realistic SOZ identification, it is necessary to identify the SOZ of an unknown patient using another patient’s electroencephalogram (EEG). However, in such cases, the influence of individual differences in EEG becomes a bottleneck. In this paper, we propose the method with domain adaptation and source patient selection to address the issue of individual differences in EEG and improve performance. The proposed method was evaluated on intracranial EEG data from 11 patients with epilepsy caused by focal cortical dysplasia. The results showed that the proposed method significantly improved SOZ identification performance compared to existing methods without domain adaptation and source patient selection. In addition, it was suggested that data from residual-seizure patients may have adversely affected estimation performance. Visualization of the prediction on MRI images showed that the proposed method might detect SOZs missed by epileptologists.
How a Global Elective Engineering class impacts Engineering Students’ Global Efficacy

Today’s pluralistic and globalized society requires a global perspective in how we think, view ourselves, and relate to others to collaborate successfully with people with different cultural backgrounds. To support the development of a global perspective, universities cultivate international co-operations to enable international mobility for students, faculty, and staff. However, in-person mobility can be inhibited due to various reasons. To support a wider pool of students gaining international experience, modern digital mediated course formats can be used to offer virtual instead of in-person mobility. This contribution evaluates a digital mediated course for mechanical engineering students (Global Engineering, GE) held in co-operation between an US and a Portuguese university, where internationally mixed student groups working on design projects in an Hyflex setting. Asides a literature driven discussion about the need for Global Engineers and a detailed description of the GE course, a longitudinal analysis was performed to assess the impact of the GE course on the students’ development of a global perspective. The longitudinal analysis showed strong statistical support that the digitally mediated GE course improved the participating students’ global perspective, assessed with the Global Perspective Inventory (GPI), tremendously. The observed improvements were in three of the six GPI dimensions comparable to an in-person semester abroad. Limitations of the study and future investigations are discussed.
A VR-based Treadmill Training System for Post-stroke Gait Rehabilitation*

Virtual reality (VR) has the potential to enhance rehabilitation by creating simulated multiple training environments, thereby maximizing the implementation of motor learning principles. However, previous use of VR-based treadmill training to improve post-stroke gait function is limited by high cost and a lack of adherence to post-stroke gait rehabilitation principles in system design. This paper describes the development of a gait rehabilitation system that integrates treadmill gait training with VR technology to create a virtual rehabilitation setting with gait training tasks and real-time performance feedback. The proposed system targets post-stroke patients and integrates low-cost sensor and rehabilitation principles to allow remote training and maximize training efficacy.Clinical Relevance—This system is developed with an emphasis on rehabilitation and motor learning principles.
Constrained Synthetic Sampling for Augmentation of Crackle Lung Sounds

Crackles are explosive breathing patterns caused by lung air sacs filling with fluid and act as an indicator for a plethora of pulmonary diseases. Clinical studies suggest a strong correlation between the presence of these adventitious auscultations and mortality rate, especially in pediatric patients, underscoring the importance of their pathological indication. While clinically important, crackles occur rarely in breathing signals relative to other phases and abnormalities of lung sounds, imposing a considerable class imbalance in developing learning methodologies for automated tracking and diagnosis of lung pathologies. The scarcity and clinical relevance of crackle sounds compel a need for exploring data augmentation techniques to enrich the space of crackle signals. Given their unique nature, the current study proposes a crackle-specific constrained synthetic sampling (CSS) augmentation that captures the geometric properties of crackles across different projected object spaces. We also outline a task-agnostic validation methodology that evaluates different augmentation techniques based on their goodness of fit relative to the space of original crackles. This evaluation considers both the separability of the manifold space generated by augmented data samples as well as a statistical distance space of the synthesized data relative to the original. Compared to a range of augmentation techniques, the proposed constrained-synthetic sampling of crackle sounds is shown to generate the most analogous samples relative to original crackle sounds, highlighting the importance of carefully considering the statistical constraints of the class under study.
Modeling the Dynamic Characteristics of Metal-Insulator-Semiconductor based Non-Volatile Memories

In this work, we propose a model that captures the dynamic characteristics of metal-insulator-semiconductor (MIS)-based non-volatile memory (NVM). The main focus of our study is to investigate the dependence of the barrier profile (and hence, the dominant transport mechanism) on the write speed and write voltage. We evaluate the results from our model with experimental data from literature. The model and its framework can provide useful insights into the design of MIS-based NVMs with optimized performance.
Camera-based Cardiovascular Screening based on Heart Rate and Its Variability In Pre- and Post-Exercise Conditions

Analysis of heart rate (HR) and heart rate variability (HRV) in pre- and post-exercise conditions can provide useful information about the health condition of cardiovascular system. Remote photoplethysmography (rPPG) that uses a contactless camera to measure vital signs from the human face might allow for ubiquitous applications in the fitness scenario. This paper benchmarks the accuracy of HR and HRV measured by ECG, PPG and rPPG, and investigates the difference of HR and HRV parameters between individuals with/without the exercise habit in pre- and post-exercise phases. We built a fitness benchmark consisting of 14 healthy subjects to perform running exercise on a treadmill, with video and reference data recorded simultaneously. The results show that rPPG has a similar performance as PPG in both the estimation of HR and HRV. The HRV parameters (Mean IBI, SDNN, LF, VLF, and SD2) of rPPG/PPG show good agreements with the ECG reference. Subjects with a regular exercise habit show lower resting HR and smaller values of HRV parameters in pre-exercise phase, fewer changes in HR and HRV at the same exercise intensity, and faster HR recovery after exercising, as compared with those without an exercise habit. The preliminary results suggest that rPPG is a promising surrogate of PPG for screening HR and HRV before and after exercise, to indicate the performance of cardiac training.
Medical Ultrasound Image Restoration in Presence of Defective Transducer Elements

Quality of medical ultrasound images is negatively affected by common defective elements in the ultrasonic transducer arrays. In this work, radio frequency (RF) signals from weak elements with low sensitivity are restored using an adaptive nonblind signal deconvolution method. For this purpose, the electromechanical impulse response of each element in the transducer must be measured. Initially, the negative effects of the RF signals recorded from each element are removed by Wiener deconvolution of RF signals through the measured impulse responses. Then to restore the RF signals, the deconvolution result is convolved with the reference electromechanical impulse response, corresponding to the intact and ideal element. To present the performance of this method, the electrical fatigue in piezoelectric materials, as a common real example for defective elements, is simulated by reducing the piezoelectric constant and permittivity parameters based on finite element method (FEM). The electromechanical impulse responses for the intact and fatigued transducers are obtained from the simulation. Finally, the quality of reconstructed point spread function (PSF) from the transducers is evaluated. The side lobe level (SLL) and full width at half maximum (FWHM) in axial and lateral directions are measured for the reference PSF from intact transducer and also original and compensated PSFs from the fatigued transducer. The results show that the metrics are improved to an acceptable level compared to the reference PSF after applying the proposed compensation method.
SDFMAP: Neural Signed Distance Fields for Mapping and Positioning in Real-Time

Neural surface reconstruction has recently gained a bit attention due to the promising result on scene rendering. Nevertheless, most of existing approaches either treat the camera parameters as the prior during training or indirectly estimate them through structure-from-motion. To tap the potential of implicit neural networks, we present a novel end-to-end neural network, termed SDFMAP, without any prior knowledge of the scene, like pre-computed camera parameters and pretrained geometric priors. Specifically, our method adopts a single multilayer perceptron to achieve simultaneously pose estimation and indoor scene reconstruction in real-time through learning the truncated signed distance function. Comparing to the recent neural implicit vSLAM systems, our approach achieves higher tracking speed via a lightweight network. Experiments on several challenging benchmark datasets show that our SDFMAP method achieves the state-of-the-art results on camera tracking and scene reconstruction.
The harmful algae, Cochlodinium polykrikoides and Aureococcus anophagefferens, elicit stronger transcriptomic and mortality response in larval bivalves (Argopecten irradians) than climate change stressors

Abstract Global ocean change threatens marine life, yet a mechanistic understanding of how organisms are affected by specific stressors is poorly understood. Here, we identify and compare the unique and common transcriptomic responses of an organism experiencing widespread fisheries declines, Argopecten irradians (bay scallop) exposed to multiple stressors including high pCO2, elevated temperature, and two species of harmful algae, Cochlodinium (aka Margalefidinium) polykrikoides and Aureococcus anophagefferens using high‐throughput sequencing (RNA‐seq). After 48 hr of exposure, scallop transcriptomes revealed distinct expression profiles with larvae exposed to harmful algae (C. polykrikoides and A. anophagefferens) displaying broader responses in terms of significantly and differentially expressed (DE) transcripts (44,922 and 4,973; respectively) than larvae exposed to low pH or elevated temperature (559 and 467; respectively). Patterns of expression between larvae exposed to each harmful algal treatment were, however, strikingly different with larvae exposed to A. anophagefferens displaying large, significant declines in the expression of transcripts (n = 3,615; 87% of DE transcripts) whereas exposure to C. polykrikoides increased the abundance of transcripts, more than all other treatments combined (n = 43,668; 97% of DE transcripts). Larvae exposed to each stressor up‐regulated a common set of 21 genes associated with protein synthesis, cellular metabolism, shell growth, and membrane transport. Larvae exposed to C. polykrikoides displayed large increases in antioxidant‐associated transcripts, whereas acidification‐exposed larvae increased abundance of transcripts associated with shell formation. After 10 days of exposure, each harmful algae caused declines in survival that were significantly greater than all other treatments. Collectively, this study reveals the common and unique transcriptional responses of bivalve larvae to stressors that promote population declines within coastal zones, providing insight into the means by which they promote mortality as well as traits possessed by bay scallops that enable potential resistance.