Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of the Human Arm Stiffness Estimation Method Developed for Overground Physical Interaction Experiments

To build a physically interactive robot for overground applications, it is crucial to first understand the biomechanics of humans underlying overground physical human-robot interaction (pHRI) tasks. Estimating human arm stiffness during overground interactive tasks is a promising first step toward this goal. For this, an arm stiffness estimation technique was developed in our previous works that consider the unique challenges involving overground pHRI, such as the need to estimate the arm stiffness from a short duration of data with fewer repetitions. In this work, our stiffness estimation method is further validated with a passive spring setup with known stiffness values, as well as with a human experiment setup that resembles the widely used seated reaching tasks. Results show that our method can estimate the passive spring stiffness within 0.5% of error. We also show that the human arm stiffness measured through our method is comparable to those reported in well-known literature. In addition, our method was able to discern experimental conditions such as early vs. late trials or differences in arm movement conditions. Implications of these results are discussed further.Clinical Relevance— This method can aid the design and development of overground interactive robots for human movement assistance and rehabilitation.
A Knowledge-based and Data-driven Approach for Predicting Acute Kidney Injury in Patients with Heart Failure*

It has great potential to integrate medical knowledge and electronic health record data for diagnosis prediction. However, present studies only utilized information from knowledge graphs, omitting potentially significant global graph structural features. In this study, we proposed a knowledge and data integrating modeling approach to reconstruct patient electronic health record data with graph structure and use medical knowledge as internal information of patient data to build a risk prediction model for acute kidney injury in patients with heart failure based on graph neural networks. Experimental results based on the MIMIC III data showed that the method proposed was superior to other baseline models in predicting the risk of acute kidney injury in heart failure patients, with an accuracy of 0.725 and an F1 score of 0.755. This study provides a novel approach to the disease risk prediction models that integrates medical knowledge and data.
Simulation of Two Metal- Semiconductor- Metal Photodetectors for Sensing Power and Angle of Incident Light

Photodetectors convert the optical signal into a proportional electrical current. Based on the vector nature of light, Photodetectors are designed to detect power and can be used in circuits to sense incident angle of radiant light. In this paper, a novel photodetector based on using two parallel circular metal-semiconductor-metal has been proposed which measure simultaneously the power and incident can angle of light. Each of these photodetectors is made of a silicon nanowire placed by thin gold tubes on a silicon oxide substrate. Examination of the absorption spectrum reveals that different resonances occur in the visible light spectrum. Therefore, because of the short distance between two nanowires, similar resonant modes are coupled with each other. The behavior of resonant modes can introduce based on the coupling mode theory. Assuming light illumination on the structure, the angle of incident light is obtained which is related to some factors including (1) the relationship between the ratio of energy stored in nanowires, (2) the parameters extracted from the coupled mode theory, (3) the wavelength of resonant mode, (4) the wavelength of radiant light, and (5) the distance between photodetectors. In addition, the total power is obtained by gathering the amount of absorbed power from each nanowire. The proposed structure acts as a simple and extremely small light sensors which shows more information about the environment and could be exploited in the next-generation of smart optoelectronic and artificial intelligence systems in order to design the imaging devices, 3D positioning, autonomous vehicles, and in robotics.
Novel Gripper with Rotatable Distal Joints for Home Robots: Picking and Placing Tableware

A convenient situation can be realized if home robots replace housework. However, tasks in an actual home environment are challenging for robots. Particularly, cleaning the table after eating is challenging because of the cluttered environments and various tableware shapes. This study presents a new type of gripper appropriate for picking and placing various tableware in narrow and cluttered environments. The gripper comprises a 1-DOF gripper and 2-DOF distal joints. The rotatable distal joints enable reducing the effective workspace when reorientating tableware and accessing tableware in narrow spaces. In addition, the gripper can transform into three types of grasping modes by actively rotating the distal joints for handling three types of tableware, namely cutlery, cups, and dishes. The gripper is experimentally demonstrated to handle various tableware and the tables can be cleaned after eating in real-world environments.
Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection

The majority of genes have a genetic component to their expression. Elastic nets have been shown effective at predicting tissue-specific, individual-level gene expression from genotype data. We apply principal component analysis (PCA), linkage disequilibrium pruning, or the combination of the two to reduce, or generate, a lower-dimensional representation of the genetic variants used as inputs to the elastic net models for the prediction of gene expression. Our results show that, in general, elastic nets attain their best performance when all genetic variants are included as inputs; however, a relatively low number of principal components can effectively summarize the majority of genetic variation while reducing the overall computation time. Specifically, 100 principal components reduce the computational time of the models by over 80% with only an 8% loss in R2. Finally, linkage disequilibrium pruning does not effectively reduce the genetic variants for predicting gene expression. As predictive models are commonly made for over 27,000 genes for more than 50 tissues, PCA may provide an effective method for reducing the computational burden of gene expression analysis.
Evaluation of Correlation between Psychomotor Vigilance Task Scores and Drowsiness Estimation Levels Obtained from Facial Videos

This paper reports the results of an experiment to evaluate the relationship between results obtained with a drowsiness estimation system we have developed using facial videos and those obtained with the Psychomotor Vigilance Task (PVT), which is a standard index of sleepiness used in sleep medicine. The correlation between PVT scores and the output of the drowsiness estimation system, which outputs drowsiness levels from assigned facial expressions, was calculated using data from 30 subjects. The Spearman's correlation coefficients between the drowsiness estimation results and the PVT mean response time, the slowest 10% response time, and the number of lapses were 0.36 (p <0.001), 0.43 (p <0.001), and 0.40 (p <0.001), respectively. Since this experiment showed a correlation between the drowsiness estimation results and those with PVT, it would seem possible to make specific interventions based on drowsiness estimation results learned from ground-truth drowsiness levels. Such estimation results could help prevent accidents resulting from drowsiness or insufficient vigilance while driving or working.
A Case-Study Based Overview of Unmanned Surface Vessel Design and Components

This paper aims to give the reader insight into the major components and design tradeoffs in Unmanned Surface Vessels (USVs). There will be a particular focus on the control and navigation systems. In order to ensure a broad range of applications are discussed, we will focus on two common but widely distinct USV classes, namely the coastal and ocean-going USVs. The first class of USVs that will be discussed are coastal ocean observation platforms with an operational range of a few tens of miles, performing shorter missions from a mother vessel or nearby harbor. As a concrete example, the custom USV built by the University of Southern Mississippi (USM) Roger F. Wicker Center for Ocean Enterprise will be used. This custom USV will perform ocean sensing missions in the USM's CUBEnet test range [1]. The second class of USVs presented are ocean-going observation platforms with global operational range, capable of crossing large stretches of oceans. For this class ‘Mahi Two’, an unmanned surface vessel developed by the team of Project Mahi [2], will be used as a concrete example. Mahi Two is the first autonomous vessel that crossed the Atlantic Ocean purely on solar power while collecting and transmitting live ocean data.
Screening and indexing of dominance of fungal flora on sandstones with special reference to historical monuments

The west facing Mahadev temple situated in a small village called Tuman. This village is situated in Chhattisgarh state of India. This west facing temple built of sand stone consists of a Garbhgriha and a mandapa, presently both in dilapidated state of preservation. The square sanctum houses a shivlinga and is approached through an ornate entrance adorned with images of Ganga and Yamuna accompanied by Saiva dwarapalas. Images of Brahma, Vishnu, Navagraha and Siva on the Lalatabimba and various incarnations of Vishnu on the door jambs are making the entrance more ornate. The jangha portion separated from the pada by vandhana mouldings has two segments embellished with geometrical and floral motifs, diamond designs, trampling lion, Bhara vahakas, marching elephants etc. A sand stone image of Nandi is situated outside the temple facing its entrance. The ISSN: 2319-7706 Volume 2 Number 10 (2013) pp. 99-105 http://www.ijcmas. com
Viterbi Demodulation of MSK Signal under both Impulsive Noise and Gaussian White Noise

Minimum shift keying (MSK) is a continuous phase modulation with both high spectrum efficiency and power efficiency, and has been widely used in wireless communication systems. Existing demodulation algorithms of MSK signals mainly consider either white Gaussian noise (WGN) or non-Gaussian impulsive noise (IN). However, the MSK signals suffer from the mixture of WGN and IN in many practical scenarios. For these environments, directly applying these demodulation algorithms could not achieve desirable bit error rate (BER) performance. In this paper, we address the problem of demodulation of MSK signals under the mixture of WGN and IN. Based on the approximate probability density function, the Viterbi algorithm based demodulation scheme is proposed by calculating the maximum-likelihood (ML) branch metric and the corresponding theoretical BER performance is analyzed. Furthermore, several popular branch metrics are compared in terms of both BER and complexity. Simulations results show that the proposed ML branch metric can obtain the best BER performance under all the scenarios, and the theoretical BER matches the simulation results well.
LB-L2L-Calib 2.0: A Novel Online Extrinsic Calibration Method for Multiple Long Baseline 3D LiDARs Using Objects

In V2X (Vehicle-to-Everything), one important work is to extrinsically calibrate multiple 3D LiDARs, which are mounted with a long baseline and large viewpoint-difference at the road-side. Current solutions either require a specific target being set up (e.g., a sphere), or require specific features existing in the environment (e.g., mutually orthogonal planes). However, it is time-consuming, sometimes even inconvenient, to set up specific targets, e.g., at busy intersections and highways. Furthermore, specific features do not always exist in the traffic scenario. Thus, the current solutions are not feasible. To address this problem, a novel extrinsic calibration method is proposed in this paper, namely LB-L2L-Calib 2.0. It is the 2.0 version of our previous work. The novelties are: 1) We propose to use the easily accessible objects on the road as features for calibration (i.e., the vehicles). Thus, it is not necessary to set up any specific targets and we do not need to worry whether specific features exist or not. The key point is we observed that the 3D bounding box centers of the vehicles are viewpoint-invariant from different viewpoints, which makes them ideal features for long baseline and large viewpoint-difference calibration. 2) To establish correct correspondence between the bounding box centers detected from different LiDARs, we propose an exhaustive searching strategy. It can robustly output correct correspondence. Extensive experiments are performed in three scenarios (simulation: intersection, real: carpark and highway), with two types of LiDAR (Velodyne and Livox), demonstrating that LB-L2L-Calib 2.0 is robust, effective, and accurate.
Advancing technologies and federating communities: a study on authentication and authorisation platforms for scientific resources in Europe

Neither the European Commission nor any person acting on its behalf is responsible for the use which might be made of the information contained in the present publication. The European Commission is not responsible for the external web sites referred to in the present publication. The views expressed in this publication are those of the authors and do not necessarily reflect the official European Commission view on the subject.
Lightweight Semantic Segmentation Network for Semantic Scene Understanding on Low-Compute Devices

Semantic scene understanding is beneficial for mobile robots. Semantic information obtained through onboard cameras can improve robots' navigation performance. However, obtaining semantic information on small mobile robots with constrained power and computation resources is challenging. We propose a new lightweight convolution neural network comparable to previous semantic segmentation algorithms for mobile applications. Our network achieved 73.06% on the Cityscapes validation set and 71.8% on the Cityscapes test set. Our model runs at 116 fps with $\mathbf{1024\mathrm{x}2048}$, 172 fps with $1024\mathrm{x}1024$, and 175 fps with $720\mathrm{x}960$ on NVIDIA GTX 1080. We analyze a model size, which is defined as the summation of the number of floating operations and the number of parameters. The smaller model size enables tiny mobile robot systems that should operate multiple tasks simultaneously to work efficiently. Our model has the smallest model size compared to the real-time semantic segmentation convolution neural networks ranked on Cityscapes real-time benchmark and other high performing, lightweight convolution neural networks. On the Camvid test set, our model achieved a mIoU of 73.29% with Cityscapes pre-training, which outperformed the accuracy of other lightweight convolution neural networks. For mobile applicability, we measured frame-per-second on different low-compute devices. Our model operates 35 fps on Jetson Xavier AGX, 21 FPS on Jetson Xavier NX, and 14 FPS on a ROS ASUS gaming phone. $1024\mathrm{x}2048$ resolution is used for the Jetson devices, and $512\mathrm{x}512$ size is utilized for the measurement on the phone. Our network did not use extra datasets such as ImageNet, Coarse Cityscapes, and Mapillary. Additionally, we did not use TensorRT to achieve fast inference speed. Compared to other real-time and lightweight CNNs, our model achieved significantly more efficiency while balancing accuracy, inference speed, and model size.
Accelerated Black-Blood Cine MR Imaging with Low-Rank and Sparsity Constraints

Black-blood MRI is a promising imaging technique for assessing vascular diseases (e.g., stroke). Vessel wall dynamic characterization using black-blood cine MRI has been recognized as an effective tool for studying vascular diseases. However, acquiring time-resolved 3D vessel wall images often requires a long acquisition time, which limits its clinical utility. In this work, we develop a new method to achieve rapid, time-resolved 3D black-blood cine MRI. Specifically, the proposed method performs (k, t)-space undersampling to accelerate the volumetric data acquisition process. Moreover, it utilizes an image reconstruction method with low-rank and sparsity constraints to enable high-quality image reconstruction from highly-undersampled data. We validate the performance of the proposed method with 3D in vivo black-blood cine MRI experiments and show representative results to demonstrate the utility of the proposed method.
A Process for Non-Rigid Fine Object Registration Based on Open3D

The massive demand for three-dimensional scanning of the physical world and artificial modeling makes three-dimensional reconstruction more important. This article is aimed at the three-dimensional reconstruction of the human foot, due to the displacement and the possible deformation of the foot in the scan (such as surface shrinkage and relaxation caused by respiration) and the systematic error of the scanner, the point cloud needs to introduce rigid registration and non-rigid registration to improve the accuracy of subsequent mesh reconstruction. The main tasks include: providing a point cloud stitching algorithm that calculates the overlapping parts of adjacent scan frames and then uses non-rigid registration; practically proposing a set of fine small pieces of rigid and non-rigid objects 3D reconstruction of the system scheme. Experimental results show that point cloud stitching using non-rigid registration improves the accuracy of modeling compared with direct point cloud stitching.
Early-warning of Cardiac Condition through Detection of Murmur in Heart Sound - A Case Study

Cardiovascular disease, particularly Rheumatic Heart Disease (RHD), is one of the leading causes of death in many developing countries. RHD is manageable and treatable with early detection. However, multiple countries across the globe suffer from a scarcity of experienced physicians who can perform screening at large scales. Advancements in machine learning and signal processing have paved way for Phonocardiogram (PCG)-based automatic heart sound classification. The direct implication of such methods is that it is possible to enable a person without specialized training to detect potential cardiac conditions with just a digital stethoscope. Hospitalization or life-threatening situations can be dramatically reduced via such early screenings. Towards this, we conducted a case study amongst a population from a particular geography using machine learning and deep learning methods for the detection of murmur in heart sounds. The methodology consists of first pre-processing and identifying normal vs. abnormal heart sound signals using 3 state-of-the-art methods. The second step further identifies the murmur to be systolic or diastolic by capturing the auscultation location. Abnormal findings are then sent for early attention of clinicians for proper diagnosis. The case study investigates the efficacy of the automated method employed for early screening of potential RHD and initial encouraging results of the study are presented.
Investigation of Non-contact Electrodes for Electrocardiogram Monitoring

This work investigates the use of an active noncontact electrode to monitor electrocardiogram (ECG) signals in the chest region of the human body across different interface materials. Non-contact electrodes provide an advantage over wet gel electrodes as they do not require direct contact with the skin which can lead to skin irritation. The key aspects and parameters of non-contact electrodes were discussed. Additionally, a printed circuit board (PCB) electrode was designed and prototyped. Experimental evaluations were performed to demonstrate the feasibility of the design as well as the impact of various interface materials. Results showed that R-peaks can be detected through all materials tested. The highest noise levels were observed in polyester, resulting in the highest signal standard deviation, followed by merino and cotton. The patch sensor with just a solder mask as an insulation layer provided the clearest ECG signal, with 100% accuracy on Rpeak detection.Clinical relevance— Non-contact electrodes offer a more comfortable solution for long-term heart monitoring with minimal discomfort due to less skin irritation when compared to conventional electrodes.
Multi-Segment Leads To Reduce RF Heating in MRI: A Computational Evaluation at 1.5T and 3T

Implanted neurostimulators are currently in widespread use and allow patients to receive therapeutic nerve stimulation for a variety of conditions. Such devices often make use of long leads extending from the device to the relevant nerve to deliver their stimulation. These leads carry a significant radiofrequency (RF) safety concern for patients who also receive magnetic resonance imaging (MRI) scans. The incident RF energy from the MRI body coil can couple with the lead and produce dangerous levels of heating at the tip of the lead during a scan. Recent studies have shown one useful approach to mitigate this heating is to vary the conductivity of the wire along its length to decrease the coupling of the incoming RF energy from the MRI coil with the long lead. In this study, we adopt a similar approach and extend it by segmenting a long cylindrical lead model into two sections of differing conductivities and assessing the maximum 1g specific absorption rate (SAR) at the lead tip at both 64 MHz and 127 MHz. We also evaluated the effect of insulation thickness as well as conductivity of the phantom on the maximum 1g SAR. An 11-fold reduction in the SAR was achieved when using high conductivity ratios between the two wire segments for the 127 MHz coil and a 2-fold reduction was seen for the 64 MHz coil.Clinical relevance— Design of an implantable segmented lead has potential to mitigate RF heating concerns and open a wider patient population to both 1.5T and 3T MRI scans.
MCM-CASR: Novel Alert Correlation Framework for Cyber Attack Scenario Reconstruction Based on NLP, NER, and Semantic Similarity

Cyber attack scenario reconstruction plays a crucial role in understanding and mitigating security breaches. In this paper, we propose a novel framework that leverages Natural Language Processing (NLP), specifically Named Entity Recognition (NER), and semantic similarity techniques to reconstruct cyber attack scenarios. By analyzing Intrusion Detection alerts, our offline approach identifies relevant entities, detects relationships between them, and measures semantic similarity to uncover hidden patterns and connections. We demonstrate the effectiveness of our framework through experimental evaluations using a public dataset. The results highlight the potential of NLP-based approaches in cyber attack scenario reconstruction.
Design of a Configurable 16-Electrode Sense and Stimulation Neuromodulation System

Electrical sensing and stimulation of nervous system activity is a valuable tool to investigate neural activity both in vivo and in vitro. A general system capable of supporting users across a variety of use cases would be valuable for the field of neuroscience. We propose a new system capable of supporting a variety of experimental cases including low and high impedance electrodes with stimulation amplitudes up to multiple mA. The system is designed to support sampling frequencies up to 26 kHz and to maximize stimulation flexibility with an electrically isolated system.