Miracle-dz/UniFAR-FTUs huggingface dataset:
Loop-Closure Detection with 3D LiDAR Data for Extreme Viewpoint Changes

Loop closure detection remains a challenging task for future autonomous robots operating in outdoor scenarios. When compared with RGB cameras, LiDAR sensors appear to be more appropriate for applications that involve significant environmental changes and various illumination conditions, facilitating more robust correlation of sensor measurements and estimation of the robot's global localization. The paper at hand presents a 3D point cloud-based method for loop closure detection that is tolerant to extreme viewpoint changes. Our method utilizes local 3D geometrical descriptors to tackle scenarios where the robot passes from the same place, yet with completely opposite direction, and is capable of understanding the similarity of the revisited area, in complete absence of common visual data in respective RGB images. To achieve this, rotation invariant Fast Point Feature Histograms (FPFHs) calculated over the Unsupervised Stable Interest Point Detection (USIP) keypoints formulate a descriptor matrix, upon which the similarity score for previously re-visited scenes is calculated. Probabilistic voting is applied to extract the top loop closure candidates and a geometric validation step is used for the final matching decision. Our method has been extensively verified on the state-of-art MulRan dataset as well as in a custom-built dataset11Publicly available dataset: https://github.com/dalexiou48/cav_pr_dataset acquired from an autonomous vehicle, that focuses on opposite traversing routes using a low-resolution LiDAR sensor.
A proposed computer vision model for running gait assessment

Running gait assessment is critical in performance optimization and injury prevention. Traditional approaches to running gait assessment are inhibited by unnatural running environments (e.g., indoor lab), varied assessor (i.e., subjective experience) and high costs with traditional reference standard equipment. Thus, development of valid, reproduceable and low-cost approaches are key. Use of wearables such as inertial measurement units have shown promise but despite their flexible use in any environment and reduced cost, they often retain complexities such as connectivity to mobile platforms and stringent attachment protocols. Here, we propose a non-wearable camera-based approach to running gait assessment, focusing on identification of initial contact events within a runner's stride. We investigated different artificial intelligence and object tracking approaches to determine the optimal methodology. A cohort of 40 healthy runners were video recorded (240FPS, multi-angle) during 2-minute running bouts on a treadmill. Validation of the proposed approach is obtained from comparison to manually labelled videos. The computing vision approach can accurately identify initial contact events (ICC(2,1) = 0.902).
Unstable Circadian Rhythm of Heart Rate of Alzheimer Dementia Based on Biological Data of Mattress Sensor

It is important to detect daily Alzheimer dementia (AD) possibility using unconstrained mattress sensors because dementia takes time before subjective symptoms appear and the main treatment is to slow the rate of progression. Forcusing on circadian rhythm disorder which tend to occur with AD, this paper analyzes the features of unstable circadian rhythms of heart rate associated with its weakening and misalignment in AD. It is found that the external feature of heart rate of AD that of up and down changes seen frequently compared to that of healthy subjects makes the multiple types of effects on the estimation process of the AD detection method based on the circadian instability represented by the trigonometric regression equation estimated from the heart rate. So, we designed two feature values from the effects and analyzed them in an analysis experiment. An analysis experiment was conducted on the heart rate of total 72 days data of one AD patient and total 30 days data of 21 healthy people, and the result confirmed significant differences between the AD and the healthy people at from 0.01% to 0.1 % level for the first feature and at from 0.1% to 5% level for the second feature. And the result shows the possibility that differences in the heart rate features is found between AD and healthy people.
Speech Tracking in Complex Auditory Scenes with Differentiated In- and Out-Field-Of-View Processing in Hearing Aids

In naturalistic auditory scenes, relevant information is rarely concentrated at a single location, but rather unpredictably scattered in- and out-field-of-view (in-/out-FOV). Although the parsing of a complex auditory scene is a fairly simple job for a healthy human auditory system, the uncertainty represents a major issue in the development of effective hearing aid (HA) processing strategies. Whereas traditional omnidirectional microphones (OM) amplify the complete auditory scene without enhancing signal-to-noise-ratio (SNR) between in- and out-FOV streams, directional microphones (DM) may greatly increase SNR at the cost of preventing HA users to perceive out-FOV information. The present study compares the conventional OM and DM HA settings to a split processing (SP) scheme differentiating between in- and out-FOV processing. We recorded electroencephalographic data of ten young, normal-hearing listeners who solved a cocktail-party-scenario-paradigm with continuous auditory streams and analyzed neural tracking of speech with a stimulus reconstruction (SR) approach. While results for all settings exhibited significantly higher SR accuracies for attended in-FOV than unattended out-FOV streams, there were distinct differences between settings. In-FOV SR performance was dominated by DM and SP and out-FOV SR accuracies were significantly higher for SP compared to OM and DM. Our results demonstrate the potential of a SP approach to combine the advantages of traditional OM and DM settings without introduction of significant compromises.
Micromagnetic configurations and switching mechanism in Pac-man-shaped submicron Ni80Fe20 magnets

Micromagnetic modeling analysis and magnetic force microscopy studies were performed in order to characterize the magnetization configuration and magnetic switching behavior in two types, PM I and PM II, of submicron Pac-man-shaped Ni80Fe20 magnetic elements. It was found that a slight variation in the shape of the elements has a striking influence on the internal magnetic structures and switching field distribution. In particular, the vortex-formation driven switching is replaced by quasicoherent reversal by removing the central core part at the center of element. The sensitive dependence of remanent magnetic configuration and switching behavior on sample geometry is discussed in terms of the competition between the exchange and demagnetizing energy terms.
Lightweight Federated Reinforcement Learning for Independent Request Scheduling in Microgrids

With the development of technology and society, the traditional energy system has become difficult to meet the demand. Applying Deep Reinforcement Learning (DRL) to solve scheduling problems in microgrid cluster edge-cloud collaborative architecture provides a new solution. However, there is no research work has been completed to address the independent training, deployment, and inference of DRL in the microgrid cluster scenario. In this paper, we propose a federated DRL-based request scheduling algorithm for distributed microgrid cluster scenarios with the goal of maximizing the long-term utility of the system. In addition, we prune the DRL model to make it more applicable to resource-constrained edge nodes. The experimental results show that the proposed algorithm has more stable performance and better adaptability to the dynamic system environment compared to the traditional centralized training. In addition, the pruning of the model compresses the size of the model to 50.4% with a 4% loss of accuracy.
CranGAN: Adversarial Point Cloud Reconstruction for patient-specific Cranial Implant Design

Automatizing cranial implant design has become an increasingly important avenue in biomedical research. Benefits in terms of financial resources, time and patient safety necessitate the formulation of an efficient and accurate procedure for the same. This paper attempts to provide a new research direction to this problem, through an adversarial deep learning solution. Specifically, in this work, we present CranGAN - a 3D Conditional Generative Adversarial Network designed to reconstruct a 3D representation of a complete skull given its defective counterpart. A novel solution of employing point cloud representations instead of conventional 3D meshes and voxel grids is proposed. We provide both qualitative and quantitative analysis of our experiments with three separate GAN objectives, and compare the utility of two 3D reconstruction loss functions viz. Hausdorff Distance and Chamfer Distance. We hope that our work inspires further research in this direction. Clinical relevance— This paper establishes a new research direction to assist in automated implant design for cranioplasty.
Motion- Based Respiratory Rate Estimation with Motion Artifact Removal Using Video of Face and Upper Body

Respiratory rate (RR) is a significant indicator of health conditions. Remote contactless measurement of RR is gaining popularity with recent respiratory tract infection awareness. Among various methods of contactless RR measurement, a video of an individual can be used to obtain an instantaneous RR. In this paper, we introduce an RR estimation based on the subtle motion of the head or upper chest captured on an RGB camera. Motion-based respiratory monitoring allows us to acquire RR from individuals with partial face coverings, such as glasses or a face mask. However, motion-based RR estimation is vulnerable to the subject's voluntary movement. In this work, adaptive selection between face and chest regions plus a motion artifact removal technique enables us to obtain a much cleaner respiratory signal from the video recordings. The average mean absolute error (MAE) for controlled and natural breathing is 1.95 BPM using head motion only and 1.28 BPM using chest motion only. Our results demonstrate the possibility of continuous monitoring of breathing rate in real-time with any personal device equipped with an RGB camera, such as a laptop or a smartphone.
Mixture of Symmetric Stable Distributions for Macular Pathology Detection in Optical Coherence Tomography Scans

Optical coherence tomography (OCT) is widely used to detect retinal disorders. In this study a new methodology is proposed for automatic detection of macular pathologies in the OCT images. Our approach is based on modeling the normal and abnormal OCT images with α-stable mixture model represented by stochastic differential equations (SDE). Parameters of the model are used to detect abnormal OCT images. The α-stable mixture model is created after applying a fractional Laplacian operator to the image and Expectation-Maximization (EM) algorithm is applied to estimate its parameters. The classification of an OCT image as normal or abnormal would be done by training SVM classifier based on estimated parameters of the mixture model. This method is examined for macular abnormality detection such as AMD, DME, and MH and achieve maximum accuracy of 97.8%. Clinical Relevance - This study establishes automatic method for anomaly detection on OCT images and provides fast and accurate OCT interpretation in clinical application.
A SAR Remote Sensing Image Change Detection Method Based on DR-UNet-CRF Model

A Synthetic aperture radar (SAR) image change detection method based on DR-UNet-CRF iterative structure is proposed by introducing a regional dynamic convolutional network to address the problems of semantic information fading phenomenon and indeterminacy of change boundaries due to differential image computation in remote sensing image change detection. Firstly, a DR-UNet segmentation network based on the dynamic region-aware convolution (DRConv) kernel is conceived to supply a univalent potential function for the conditional random field, and a guide-mask generation method guided mask generation method with feature pyramid network (FPN) based structure is presented to guide an improved dynamic convolutional UNet to obtain accurate remote sensing change regions by learning fine spatial region delineation. Secondly, the pair-wise potential function based on image grayscale features and spatial features is designed to model the inter-pixel relationship. Finally, we use a fully connected conditional random field (CRF) model to iteratively optimize for change regions to achieve semantic compensation, thus defining the boundaries of remote sensing images more precisely. By comparing with the mainstream change detection methods, it can be considered that method in this paper has better detection performance.
The Effects of Deep Brain Stimulation on Motor Unit Activities in Parkinson's Disease based on High-Density Surface EMG Analysis

Tremor in Parkinson's disease (PD) is caused by synchronized activation bursts in limb muscles. Deep Brain Stimulation (DBS) is an effective clinical therapy for inhibiting tremor and improving movement disorders in PD patients. However, the neural mechanism of how tremor symptom is suppressed by DBS at motor unit (MU) level remains unclear. This paper developed a data acquisition platform for collecting physiological data in PD patients. Both high-density surface Electromyography (HD-sEMG) and kinematics data were collected concurrently before and after DBS surgery. The MU behaviors were obtained via HD-sEMG decomposition algorithm to reveal the effect of DBS on PD tremor. A data set of one tremor dominant PD patient acquired in pre-operation and post-operation (DBS-on) phases was analyzed. Preliminary results showed significant changes in MU firing rate and MU synchronization. The analysis approach introduced in this paper provides a novel perspective for studying the neural mechanism of DBS as revealed by MU activities. Clinical Relevance- This study presented an approach to investigate the effect of DBS therapy on improving tremor disorder of PD patients.
Machine Learning Algorithms For COVID-19 Fake News Detection

Fake news corresponds to distributed information which is not true. It becomes popularized during the 2016 U.S. elections. With the spread of COVID-19 and becoming an epidemic, much information is exchanged around the world. A part of this information is fake having a negative impact on mental health and psychological well-being of people. Because of the importance of this issue, we propose in this work applying several machine learning algorithms to detect COVID-19 fake news. We propose, also, several metrics to evaluate those models and to choose the best among them. Compared to the existing works, we use four classes: Fake, Mostly Fake, True and Mostly True.
Hybrid Edge-Artificial Intelligence Model for Identification and Classification of Brain Tumours from Computed Tomography Scans

Profound diagnosis of Computed Tomography (CT) scans play a vital role in medical diagnosis among which brain scans help in identifying several neurological problems. We propose a solution by implementing a hybrid edge AI model to identify and classify brain tumours from CT scans. The deep learning architecture developed in the current research is a combination of Convolutional Neural Network(CNN)-based "Mobile Net" architecture integrated with eXtreme Gradient Boost (XGboost) architecture. While traditional computation standards rely on mobile sensing and cloud computing, edge AI implementation on portable devices provides numerous advantages such as quick response time and enhanced data privacy.
A Wearable-based Preventive Model To Promote Oral Health Through Personalized Notification

Wearable technology has great potential to develop human-centric healthcare applications. It reshapes our lifestyle by providing information related to physical activities, sleep monitoring, or heartbeat rhythms. In this paper, we present an innovative model for oral healthcare, which is prevention-focused to notify individuals about cleaning teeth activity. It is based on a wrist-worn accelerometer device and has two components—first, a computationally lightweight feature extractor—secondly, a robust feed-forward neural network to recognize the cleaning teeth activity. The model performance is measured using standard performance metric $F_{1}$ - score (i.e., 98%), which shows the applicability in a real-life scenario. The trained model can reside inside the smartwatch as a wrist-worn wearable. It would generate personalized notifications if s/he skipped the toothbrush activity. Furthermore, it notifies the users to change the toothbrush after three months, reducing the cognitive burden. Clinical Relevance—This work help the individuals to maintain their oral health using smartwatches as a wearable device
Math Skills: a New Look from Functional Data Analysis

Mental calculations involve various areas of the brain. The frontal, parietal and temporal lobes of the left hemisphere have a principal role in the completion of this typology of tasks. Their level of activation varies based on the mathematical competence and attentiveness of the subject under examination and the perceived difficulty of the task. Recent literature often investigates patterns of cerebral activity through fMRI, which is an expensive technique. In this scenario, EEGs represent a more straightforward and cheaper way to collect information regarding brain activity. In this work, we propose an EEG based method to detect differences in the cerebral activation level of people characterized by different abilities in carrying out the same arithmetical task. Our approach consists in the extraction of the activation level of a given region starting from the EEG acquired during resting state and during the completion of a subtraction task. We then analyze these data through Functional Data Analysis, a statistical technique that allows operating on biomedical signals as if they were functions. The application of this technique allowed for the detection of distinct cerebral patterns among the two groups and, more specifically, highlighted the presence of higher levels of activation in the parietal lobe in the population characterized by a lower performance.
An Intra- and Inter-Modality Fusion Model Using MR Images for Prediction of Glioma Isocitrate Dehydrogenase (IDH) Mutation

According to the 2016 World Health Organization(WHO) Classification scheme for gliomas, isocitrate dehydrogenase(IDH) is a very important basis for diagnosis. There is a strong relationship between IDH mutation status and glioma prognosis. Therefore, it is important to predict the IDH mutation status for preoperatively treating glioma. In the past decade, there has been an increase in the use of machine learning, particularly deep learning, for medical diagnosis. To date, many methods using either deep learning or radiomics have been proposed for predicting glioma IDH mutation status. In this study, we proposed an intra- and inter-modality fusion model, which first fuses both Magnetic Resonance Imaging-based (MRI-based) radiomics with deep learning features in each modality (intra-modality fusion) and then the prediction results from each modality are fused by using an inter-modality regression model, to improve the IDH status prediction accuracy. The effectiveness of the proposed method is validated via our private glioma data set from the First Affiliated Hospital of Zhengzhou University (FHZU) in Zhengzhou, China. Our proposed method is superior to current state-of-the-art methods with an accuracy of 0.77, precision of 0.77, recall of 0.77, and F1 score of 0.77, thereby exhibiting an 8% increase in accuracy to predict the IDH mutation status for glioma treatment.
Tiny CNN for Seizure Prediction in Wearable Biomedical Devices

Epilepsy is a life-threatening disease affecting millions of people all over the world. Artificial intelligence epileptic predictors offer excellent potential to improve epilepsy therapy. Particularly, deep learning models such as convolutional neural networks (CNN) can be used to accurately detect ictogenesis through deep structured learning representations. In this work, a tiny one-dimensional stacked convolutional neural network (1DSCNN) is proposed based on short-time Fourier transform (STFT) to predict epileptic seizure. The results demonstrate that the proposed method obtains better performance compared to recent state-of-the-art methods, achieving an average sensitivity of 94.44%, average false prediction rate (FPR) of 0.011/h and average area under the curve (AUC) of 0.979 on the test set of the American Epilepsy Society Seizure Prediction Challenge dataset, while featuring a model size of only 21.32kB. Furthermore, after adapting the model to 4-bit quantization, its size is significantly decreased by 7.08x with only 0.51% AUC score precision loss, which shows excellent potential for hardware-friendly wearable implementation.
Propeller load simulation on gas turbine test stand

A necessary step in installing a gas turbine on a ship is to test with loads as close as possible to those exerted by the ship’s propeller. In order to reproduce this load, the braking device of the turbine test bench has to simulate a torque law identical to that exerted by the propeller in conditions as close as possible to reality. The paper herein presents the simulation of the loading of a propeller on a test bench. The propeller is simulated by reproducing the torque at a certain speed depending on the torque required by a gas turbine controlled by a gas lever.
A highly stable electroplating bath for micro-scale Sn-Ag solder bumping

The non-cyanide and highly stable Sn-Ag alloy electroplating bath with three complexing agents of Ag+ was investigated. In the electroplating bath, SnSO4 and AgNO3 were added as the main salt, and a large amount of sulfuric acid was added as the electrolyte. The complexing agents of Ag+ were composed of the amine compound (complexing agent A), complexing agent B and complexing agent C, which effectively negatively shifted the deposition potential of Ag+ while it has little effect on that of Sn2+, inhibiting the redox reaction between Sn2+ and Ag+. The Sn-(1.5~2.0) wt. %Ag films with uniform, flat and superior adhesion were obtained with the current density of 4~8 ASD. With the increase of current density, Ag content in the Sn-Ag film decreased. Meanwhile, the top-view of the film became uneven due to the bigger grains formed under the larger current density. The Sn-Ag films with uniform distribution of Ag and Sn without voids were formed by using the current density of 4 ASD, Subsequently, the Sn-Ag micro bumps with the Within-Die < 8 % were obtained on the pattern wafer after reflowing at 260 oC for 60 s.
Design of ADAS Fatigue Control System using Pynq z1 and Jetson Xavier NX

In recent years, driver fatigue and drowsiness have been presented as the main factors of road accidents with a high rate of passenger deaths, injuries and property losses. In this paper, we developed a monitoring system for vehicle drivers that detects and warns of the presence of fatigue and drowsiness through computer vision or machine vision. Our developed system consists of four steps: training of the classifier to perform the detection module, image acquisition and processing, detection and alarm activation. Firstly, training of classifier was performed using Haar technique to locate face features. Secondly, a Jetson Xavier NX or Pynq Z1 development board was used to perform real-time video using a 4K camera, thirdly, we use the training classifier to detect the face image and facial landmark algorithm proposed by Kazemi & Sullivan to detect face features. Finally, our system analyses the state characteristics of the eyes; once the processing is completed, the detection of drowsiness and fatigue is carried out.