Miracle-dz/UniFAR-FTUs huggingface dataset:
Field Demonstrations of Precision Mapping and Control by the SUNFISH® AUV in Support of Marine Archaeology

Detailed underwater inspection or sampling tasks are often performed by human divers, often in difficult or dangerous conditions. Highly maneuverable, intelligent autonomous underwater vehicles have the potential to enhance or replace the most monotonous and difficult portions of this work to improve outcomes and reduce risks to divers. In this paper, we describe autonomous exploration, mapping, return-to-target, sediment sampling, and diver assistant capabilities developed for the small, hover-capable SUNFISH® AUV to enable marine archaeological investigation in unstructured, 3-D, overhead environments. SUNFISH is a compact, easily transportable vehicle, with full 6-DOF control. It is able to navigate in and build fully 3-D maps of complex underwater geometries such as cliffs, caves, and tunnels. The primary sensing is via a side-look multibeam sonar which can be used for survey and detailed 3-$D$ mapping. Additional sensors and actuators enable photogrammetry, precision inspection, and underwater intervention in these environments. SUNFISH employs a real-time sonar SLAM system and an exploration behavior to expand the mapped volume and safely find paths through 3-$D$ environments. Here we report on augmented vehicle capabilities enabling return to targets in the mapped volume, and intervention in the form of sediment sampling at a location specified in the 3-$D$ map. In May-June 2023, we demonstrated these capabilities in a field deployment as part of the Our Submerged Past project. This project aims to uncover evidence of prehistoric human habitation on the continental shelf west of Prince of Wales Island in Southeast Alaska by exploring, mapping, and sampling currently submerged caves and rock shelters that would have been exposed at times in the last 17,000 years. In this project, the SUNFISH AUV demonstrated the ability to autonomously survey an area, return to targets identified in its high-precision 3-D map, take sediment samples, and assist human divers in archaeological tasks. Lessons learned in applying a cutting-edge AUV to investigate and document archaeological sites in this project can serve to enable new types of investigations, and streamline and reduce costs of archaeological and other assessments in the future.
GBW Optimization in Two-Stage OTAs Operating in Weak Inversion

An experimental study on the optimization of two-stage OTAs using different output stages and compensation techniques is presented. Optimization tries to maximize GBW for a given quiescent current consumption, and to this aim transistors are operating in weak inversion. A test chip prototype including 8 different two-stage OTA configurations has been fabricated in a standard 0.5-μm CMOS technology. Measurement results validate the benefits of the optimization technique employed in different design scenarios.
Multi-source Data Fusion Method of Equipment Maintainability Based on Improved DS Evidence Theory

To fully utilize multi-source data of equipment maintainability and improve the fusion accuracy of prior distributions to ensure the accuracy of maintainability verification results, a method for multi-source data fusion of equipment maintainability based on improved Dempster-Shafer (DS) evidence theory is proposed to solve the problem of multi-source data conflicts. The characteristic information of multi-source data is fully mined and extracted, and the evidence mass functions based on sample size, distribution characteristics and data importance are constructed respectively. Considering the correlation between the evidences and the uncertainty of the evidences, the angle cosine is introduced to measure the conflict degree between the evidences, and the information entropy is introduced to measure the uncertainty of the evidences. Then, combined the support degree and the uncertainty degree of evidences to jointly modify evidences, establish a multi-source conflict evidences data fusion model to realize the effective fusion of multi-source data. Finally, two cases are analyzed to verify the effectiveness and feasibility of the proposed method.
A pilot study for testing a low-cost 3D design with an inertial sensor for the quantitative assessment of finger tapping in patients with Parkinson’s Disease

Parkinson’s disease (PD) is one of the most common neurodegenerative disorders worldwide. Current identification and monitoring of its motor symptoms depends on the clinical expertise. Repetitive finger tapping is one of the most common clinical maneuvers to assess for bradykinesia. Despite the increasing use of technology aids to quantitatively characterize the motor symptoms of PD, there is still a relative lack of clinical evidence to support their widespread use, particularly in low-resource settings. In this pilot study, we used a low-cost design prototype coupled with an inertial sensor is coupled to quantify the frequency of the finger tapping movements in four participants with PD. Repetitive finger tapping was performed using both hands before and after taking levodopa as part of their clinical treatment. The proposed 3D design allowed repetitive movements to be performed without issues. The maximum frequency of finger tapping was in the range of 0.1 to 4.3 Hz. Levodopa was associated with variable changes in the maximum frequency of finger tapping. This pilot study shows the feasibility for low-cost technology to quantitatively characterize repetitive movements in people living with PD.Clinical relevance— In this pilot study, a low-cost inertial sensor coupled to a design prototype was feasible to characterize the frequency of repetitive finger tapping movements in four participants with PD. This method could be used to quantitatively identify and monitor bradykinesia in people living with PD.
Atypical scanning strategies of emotional faces for individuals with high autistic traits

Autism has become one of the primary diseases causing disability in children, and the incidence has risen rapidly in recent years. The preclinical study on individuals with high autistic traits is extremely important to reduce genetic risks of autism because high autistic traits is the susceptibility marker of autism. However, few studies explored the face scanning pattern of people with high autistic traits in typical developing populations. In this study, we designed a facial emotion recognition experiment including four emotions (happy, neutral, sad, angry) and three angles (0°, 45°, 90°) , and informed the participants to identify the facial emotion. Forty-two college students with typical development were recruited and divided into high autistic traits (HAT) group and low autistic traits (LAT) group by the Autism-Spectrum Quotient, and we collected the eye movement data using eye-tracking technology when they performed the task. The response time, recognition accuracy, AOI based proportional fixation time and pupil diameter were computed and analyzed for both groups. HATs showed significantly lower recognition accuracy and lower pupil diameter than LATs when recognizing negative emotions (P<0.05) , indicating HATs kept poor autonomic nervous arousal. What ’ s more, the proportional fixation time of HATs were significantly more in mouth area but less in eye area than that of LAT group (P<0.05) , revealed HATs had an atypical emotional faces scanning strategies that paid less attention to eyes and more attention to mouth. Our research provides a feasible objective biomarker for screening high autistic traits population.
Heart Failure Assessment Using Multiparameter Polar Representations and Deep Learning *

Heart failure refers to the inability of the heart to pump enough amount of blood to the body. Nearly 7 million people die every year because of its complications. Current gold-standard screening techniques through echocardiography do not incorporate information about the circadian rhythm of the heart and clinical information of patients. In this vein, we propose a novel approach to integrate 24-hour heart rate variability (HRV) features and patient profile information in a single multi-parameter and color-coded polar representation. The proposed approach was validated by training a deep learning model from 7,575 generated images to predict heart failure groups, i.e., preserved, mid-range, and reduced left ventricular ejection fraction. The developed model had overall accuracy, sensitivity, and specificity of 93%, 88%, and 95%, respectively. Moreover, it had a high area under the receiver operating characteristics curve (AUROC) of 0.88 and an area under the precision-recalled curve (AUPR) of 0.79. The novel approach proposed in this study suggests a new protocol for assessing cardiovascular diseases to act as a complementary tool to echocardiography as it provides insights on the circadian rhythm of the heart and can be potentially personalized according to patient clinical profile information.Clinical relevance— Implementing polar representations with deep learning in clinical settings to supplement echocardiography leverages continuous monitoring of the heart’s circadian rhythm and personalized cardiovascular medicine while reducing the burden on medical practitioners.
Performance Analysis of Rapid PVST+ Technology (Per VLAN Spanning Tree) On LAN Networks Using Redudancy Link Systems

This article discusses the application of Rapid $\text{PVST}+$ technology in Local Area Network (LAN) networks to prevent network looping. The study used an experiment in GNS3 software to simulate a computer network. The objective was to analyze the performance of Rapid $\text{PVST}+$ in forming a backup communication line, preventing network looping, and its impact on latency, throughout, packet loss, and jitter. The study found that Rapid $\text{PVST}+$ can form a backup communication line in 3 to 5 seconds and prevent network looping. The latency value is affected by the number of switches that the backup communication line passes through. The more switches, the higher the latency value. The throughput value decreases with an increase in latency. Packet loss is affected by the size of the file used in data transfer testing. Jitter is influenced by the average latency or delay variation from the data transfer test results.
Inexpensive Fabrication of Stretchable Dry EEG Electrodes Using Well Known Dry Film Photoresists

With the discovery of new brain diseases, clinical tests for electroencephalogram (EEG) have become more important. Commercially available wet Ag/AgCl electrodes apply conductive gel to the scalp of the subject before recording of the EEG signal. After applying the gel, the impedance is gradually decreased and the EEG signal can be received. In the long term, the conductive gel dries and the impedance increases again. The gel causes skin irritations in the course of time as well. Therefore, Ag/AgCl electrodes are inappropriate to be used in long term measurements. Dry electrodes are introduced as a gel free no preparation alternatives of wet electrodes in the literature. In this manuscript, a novel flexible and stretchable dry EEG electrode is demonstrated. The electrode features four conductive pads to contact the scalp. The stretchable electrodes are embedded in elastomer. A flexible polyimide layer supports the conductive electrodes to increase mechanical reliability in elongations. The electrodes are produced using a relatively straightforward and cost-effective fabrication method namely commercial dry film photolithography. The EEG signal is recorded with the fabricated electrode as well as wet Ag/AgCl electrodes. The received signals from the two electrodes feature relatively high correlation.
Denoising Cough Sound Recordings Using Neural Networks *

Objective cough sound evaluation is useful in the diagnosis and management of respiratory diseases. However, the performance of cough sound analysis models can degrade in the presence of background noises common in everyday environments. This brings forward the need for cough sound denoising. This work utilizes a method for denoising cough sound recordings using signal processing and machine learning techniques, inspired by research in the field of speech enhancement. It uses supervised learning to find a mapping between the noisy and clean spectra of cough sound signals using a fully connected feed-forward neural network. The method is validated on a dataset of 300 manually annotated cough sound recordings corrupted with babble noise. The effect of various signal processing and neural network parameters on denoising performance is investigated. The method is shown to improve cough sound quality and intelligibility and outperform conventional denoising methods.
A Proof-of-Concept Numerical Ising Machine for Neural Spike Localization

Identifying the physical locations of neurons based on the spike waveforms captured by multiple recording channels, namely spike localization, can potentially enhance spike sorting accuracy. This study proposes a new method for spike localization, where the problem is first described as a nonconvex optimization problem and then the optimization is attempted heuristically via a numerical Ising solver. The paper first presents a quadratic unconstrained binary optimization (QUBO) formulation of spike localization. Then, a MATLAB solver simulating an Ising machine is written to solve the QUBO. The proposed method is evaluated on a 2D toy problem consisting of two electrodes and a single spike event, where the neuron location search is conducted in three different regions placed at increasing distances from the electrodes. The results indicate that the neuron can be accurately identified when in one of the nearest nodes to the electrodes, whereas the accuracy decreases to 87.5% and 75% as the search region distance increases. The study for the first time formulates the spike localization problem as a QUBO and demonstrates the feasibility of solving the resultant non-convex optimization problem heuristically using an Ising machine.Clinical Relevance— High channel-count implantable neural monitoring systems allow tracking large brain regions at the cost of increased data volumes to transmit and power dissipation. The new spike localization approach presented can potentially decrease the data volume and power consumption by enabling high accuracy spike localization at the implantable system.
Harnessing the Power of Human Biomechanics in Force-Position Domain: A 3D Passivity Index Map for Upper Limb Physical Human-(Tele) Robot Interaction

In the context of physical human-(tele)robot interaction, passivity-based stabilizers have been used to guarantee the physical or (tele) physical stability. In most of these examples, human biomechanics is considered an inherently passive system that dissipates energy. This assumption may not hold true when the interaction is implemented in the force-position domain, even though such a setting would be needed to boost positional accuracy and avoid the common kinematic drifts in the force-velocity domains. The aforementioned topic is examined in this paper using the concept of shortage versus excess of passivity index for human biomechanics in the force-position domain. We also investigate the compounding effect of the frequency of interaction. The outcomes of this paper will be imperative for the design of force-position domain pURI stabilizers when the classical assumption of passivity of human biomechanics can lead to serious safety issues. In this work, for the first time, we quantitatively present the passivity margin and, thus, the energetic behavior of the human arm's biomechanics under various interaction scenarios in the Force-Position domain. The outcome of this work includes a three-dimensional passivity index map (3DPiM) that is validated on five healthy participants. The goal is to illustrate the passivity margin of the human upper limb biomechanics for two distinct levels of muscle co-contractions, as indicated by the Electromyography (EMG) signal, across four interaction frequencies and eight geometric directions. This outcome enables the future development of biomechanics-aware stabilizers in the force-position domain, quantifying the passivity margin in real-time and thus significantly reducing the stabilizer's conservatism while ensuring the safety of human-robot interactions.
DOA Estimation of Acoustic Signals Using Stacked Products of Cross-Correlations and Coherence Factor

Direction of arrival (DOA) estimation of acoustic signals has been studied extensively by many researchers. Improving the performance of the DOA estimation methods is a hot topic in array signal processing. In this study, an application of the coherence factor, which is widely applied in medical ultrasound imaging, is extended to DOA estimation of wideband acoustic signals. The advantage of applying different coherence factors in tracking acoustic sources is illustrated by a cross correlation-based DOA estimation. In order to evaluate the performance of the proposed method both simulated data and narrowband and wideband recorded signals are utilized. Simulation results reveal that combining the coherence factors with the cross-correlation DOA estimator lead to resolution improvement for both narrowband and wideband acoustic sources.
Facial Action Unit Detection using 3D Face Landmarks for Pain Detection

Automatic detection of facial action units (AUs) has recently gained attention for its applications in facial expression analysis. However, using AUs in research can be challenging since they are typically manually annotated, which can be time-consuming, repetitive, and error-prone. Advancements in automated AU detection can greatly reduce the time required for this task and improve the reliability of annotations for downstream tasks, such as pain detection. In this study, we present an efficient method for detecting AUs using only 3D face landmarks. Using the detected AUs, we trained state-of-the-art deep learning models to detect pain, which validates the effectiveness of the AU detection model. Our study also establishes a new benchmark for pain detection on the BP4D+ dataset, demonstrating an 11.13% improvement in F1-score and a 3.09% improvement in accuracy using a Transformer model compared to existing studies. Our results show that utilizing only eight predicted AUs still achieves competitive results when compared to using all 34 ground-truth AUs.
Optimization of the signal reconstruction process by multiresolution analysis for a signal with Gaussian noise using the Sure-Shrink algorithm and wavelet Daubechies

By an optimization algorithm, the signal reconstruction process is optimized using multiresolution analysis with the Daubechies mother wavelet and the Sure-Shrink thresholding algorithm. This work seeks to propose an optimization scheme and a threshold associated with the noise variance, which complements the signal reconstruction process and achieves better performances when comparatively analyzing the signal to noise ratio, the mean square error, and the norm, compared to the signal obtained from the unoptimized process.
DWT-CNNTRN: a Convolutional Transformer for ECG Classification with Discrete Wavelet Transform

Cardiovascular diseases are the leading cause of death worldwide. The diagnoses of cardiovascular diseases are usually carried out by cardiologists utilizing Electrocardiograms (ECGs). To assist these physicians in making an accurate diagnosis, there is a growing need for reliable and automatic ECG classifiers.In this study, a new method is proposed to classify 12-lead ECG recordings. The proposed model is composed of four components: the CNN(Convolutional Neural Network) module, the transformer module, the global hybrid pooling layer, and a classification layer. To improve the classification performance, the model takes the discrete wavelet transform of ECG signals as the model inputs and utilizes a hybrid pooling layer to condense the most important features over each period.The proposed model is evaluated using the test set of the China Physiological Signal Challenge 2018 dataset with 12-lead ECGs. It performs with an average accuracy of 0.86 and an average F1-scores of 0.83. The scores are particularly good for the block conditions (LBBB, RBBB, I-AVB). The main advantage of the proposed model is that, it obtains good results with a significantly smaller number of parameters compared to other individual and ensemble models.Clinical relevance— This work establishes a new ECG classifier model with high performance and low model size. It can make automatic ECG analysis more accessible, efficient, and accurate, especially in remote or underserved areas.
Toward the Development of 6G System Level Simulators: Addressing the Computational Complexity Challenge

The advancement and standardization of the cellular network system require thorough investigation, analysis, and experimentation of novel protocols, architectures, and functionalities. In this regard, computer-aided tools or simulators allow the execution of these requirements with the much-needed controllability, reproducibility, cost efficiency, and convenience. Simulators have been proven beneficial since the dawn of the cellular network era and are likely to be critical for the upcoming 6G development. However, mimicking such a complex network requires developing intricate and, at the same time, practical simulators. One of the major concerns of the existing simulators is the computational complexity of modeling a realistic and complete network. This challenge is anticipated to exacerbate with the advent of 6G considering its scope and peculiarities. In this article, we analyze the computational complexity of incoming 6G simulators and provide solutions to mitigate this issue. The presented novel framework for future simulators aims to transform the traditional way of building network simulators to serve the unprecedented demand of 6G. The presented use case highlights the efficacy of the proposed framework where we show a 100-fold improvement in the run-time performance of the innovative architecture compared to traditional simulators.
A Resnet-SVR Model for Lithium-ion Battery State of Health Estimation

Accurate battery state of health (SOH) estimation monitors the battery current health status and alerts the users to replace the battery when it has degraded to a certain level. In this study, a hybrid model based on Resnet-SVR is proposed to estimate battery SOH through constructing voltage-based battery temporal energy features during constant current charging in the form of a sliding window, making the energy data into a picture-like two-channel structure. The proposed method is validated on two battery datasets and the experimental results demonstrate that the Resnet-SVR based model has certain generalization capability and achieves satisfactory battery estimation results with an average mean absolute error of 0.0072 and mean absolute percentage error of 0.83%.