Miracle-dz/UniFAR-FTUs huggingface dataset:
Unscented Kalman Filter based Coestimation of SoC and SoHR in Lithium Battery with Hysteresis

Lithium iron phosphate batteries are the most promising energy storage systems for automotive applications thanks to their low cost, good energy density and high safety. However, they exhibit a pronounced hysteresis between charge and discharge voltage curves that significantly complicates the estimation of the battery States of Charge and Health. A state estimation algorithm based on the Unscented Kalman filter is developed in this work to estimate the State of Charge and the resistance State of Health (SoHR) of a Lithium-iron-phosphate battery. The hysteresis is modeled by a first-order charge relaxation equation. Moreover, a custom simulation platform is developed to test the estimation algorithm in automotive applications obtaining a State of Charge estimation error lower than 1% and a voltage estimation RMS error of only 1.94 mV when the battery cell is exercised with the UDDS load profile.
A Potential Nanosensing Method for Early Diagnosis of Endometrial Cancer with Sialic Acid Biomarker

Studies have shown that sialic acid is a potential biomarker that is related to diseases and cancers, including gynecological cancers. Gynecological cancers (GCs) are difficult to diagnose because of its complicated diagnosis process and the many different forms. This is a major threat to women's health. Endometrial cancer (EC) is a type of GCs with the third highest mortality rate among women. It is important to diagnose it at its early stage, so as to allow the patients to be completely treated. Our primary research was to use silver nanoparticles to demonstrate Surface Enhanced Raman Spectroscopy (SERS) as a nano-sensing method of measuring sialic acid concentration in biological fluids. Our experiments created its concentration calibration curve, and established a threshold of 0.47mg/mL for the possible early diagnosis of the EC. The SERS nano-sensing method is extremely sensitive. In our experiments it shows enhancement even at the lowest concentration of 0.015 mg/ml for the sialic acid. This study plays a significant role in the development of early diagnostic methods for EC, so as to save women's lives in the world.
An Experimental Platform for Molecular Communication Based on Light Absorption

Molecular communication (MC) is a communication paradigm that uses molecules as information carriers. Flow-assisted MC has recently gained significant attention due to the similarity of its transport mode to the way blood vessels transport substances. Although many theoretical studies on MC with drift have been conducted, there are still relatively few related experimental platform studies. With this in mind, this paper proposes an experimental platform for MC based on light absorption. We conducted experiments to verify the reliability of information transmission and investigate the impact of different parameters on system performance. The experimental results show that the platform can enable the sending and receiving of information, and its performance improves at higher flow rates. Data transmission based on light absorption is non-invasive, making it a promising approach for information transmission through the human body.
Improving Solar Cells Efficiency with PMMA-Carbon Dots Nanocomposites

Nitrogen-doped carbon nanodots synthesized from L-arginine and ethylenediamine (NCNDs) were combined to PMMA for obtaining fluorescent nanocomposites. Since this photoluminescence could increase the efficiency of solar energy concentrators and generators, photovoltaic cells were coated with such materials. Even using a setup with low contact of the optical polymer film on silicon surface, a promising 11.3% increase of cell efficiency was observed, showing the feasibility and potential for application of these environmentally friendly materials.
Proactive Eavesdropping Performance for Integrated Satellite-Terrestrial Relay Networks

This work investigates the proactive eavesdropping performance for the integrated satellite-terrestrial relay networks (ISTRNs) with multiple proactive monitors. To study the eavesdropping non-outage probability, we propose one proactive eavesdropping case/mode, where the legitimate monitors could change its roles between eavesdropping and jamming, namely, Eavesdropping then Jamming. Particularly, we get the accurate expressions of the eavesdropping non-outage probability for the proposed proactive eavesdropping mode in the presence of multiple monitors. To derive further insights, we provide the asymptotic analysis of the proposed eavesdropping non-outage probabilities. Besides, the colluding proactive eavesdropping scenario is considered, in which all the monitors cooperate to overhear or jam the legitimate users. Finally, numerical results are obtained to verify the rightness of the theoretical analysis.
POEM: Proactive Over-Exercise Monitoring

The escalating popularity of fitness, driven by advancements in fitness-related technology, has led to a rising incidence of over-exercise, resulting in an array of health concerns including injuries and burnout. Current fitness tracking apps and wearables focus mainly on documenting past workouts, neglecting the forecasting of future activities, thereby inadvertently promoting over-exercise. To counter this issue, we propose a novel application named Proactive Over-Exercise Monitoring (POEM). POEM goes beyond merely recording past workouts and proactively considers future workouts, alerting users about the likelihood of over-exercise. It derives daily workout duration from multimodal data, incorporating the user’s exercise history, location details, and daily schedules. Through its proactive alerts on potential over-exercise, POEM seeks to instill safer and more sustainable exercise practices.
iCAPD: A Deep Learning-Based Monitoring System for Continuous Ambulatory Peritoneal Dialysis

This paper proposes a deep learning-based monitoring system for continuous ambulatory peritoneal dialysis called iCAPD. iCAPD comprises a load cell module, a heating and thermal preservation module, a fluid turbidity test chart, a camera module, an Edge-AI computing module, and a display module. iCAPD can heat and preserve the temperature of the dialysate bag at 37°C within 20 minutes. Furthermore, it can detect the dialysate infusion volume by sensing the weight change of the dialysate bag. Moreover, iCAPD uses deep learning-based technology to recognize the turbidity of drainage fluid from the abdominal cavity. The experimental results show that the turbidity recognition performance of iCAPD can achieve the purpose of assisting the assessing the severity of associated peritoneal symptoms (e.g., peritonitis).
Dynamic Focus Mechanism-Based Dual-Domain Reconstruction Network for Accelerated MRI

The integration of information from both k-space and image domains has become a topic of growing interest in accelerated Magnetic Resonance Imaging (MRI) using dual-domain reconstruction networks. However, many existing methods treat sampled and unsampled measurements equally in the k-space domain, leading to progressive feature shift and suboptimal reconstruction performance. Additionally, networks with single-scale receptive field in the image domain are insufficient for fully exploring global-local information and lack channel-wise feature interaction to preserve structural details. To address these issues, we propose a novel dual-domain reconstruction network named DFRNet, which incorporates a dynamic focus mechanism. In the k-space domain, we introduce an area normalization module that dynamically eases feature shift and predicts reliable k-space measurements. In the image domain, our proposed dynamical attention module with channel-wise gating mechanism extracts rich global-local features for detail recovery. Quantitative and qualitative experiments show that our proposed DFRNet achieves competitive performance to several state-of-the-art methods while being more lightweight.
Filtering Alerts on Cloud Monitoring Systems

Recent advances in cloud computing and data centers have increased the demands for monitoring the network infrastructure and the applications that it hosts. The monitoring processes let network administrators to be aware of the status of the physical and logical units that compose their system. Since the goal of next generation networks is to minimise the administrators’ intervention, the alerting systems should minimize the frequency of notifications, emphasizing on critical scenarios such as when a monitoring metric surpasses a threshold or an anomalous behaviour is detected. However, current monitoring tools flood network administrators with hundreds of notifications every day. In this paper, we propose a binary classification approach, in order to decide if the administrators should be notified through monitoring alerts or not. To do so, our framework is build upon real monitoring logs and alerts, that show how the administrators reacted when receiving an alert. Extensive simulation results assess the performance of various classification approaches and reveal that random forests are great candidates for the binary classification alerting system that we propose, in terms of classification efficiency and computational overhead.
Semi-Supervised Learning with Out-of-Distribution Unlabeled Samples for Retinal Image Classification

Due to the lack of sufficient labeled data, semi-supervised learning is an effective scheme to boost the performance of retinal disease classification. However, traditional semi-supervised methods are restricted by the strong assumption that the labeled and unlabeled images share the same distribution. In practice, labeled and unlabeled retinal images may have different style domains (e.g. different cameras) or semantic domains (e.g. different diseases). In this paper, an out-of-distribution (OOD) semi-supervised learning method is proposed for retinal disease classification based on knowledge distillation with a teacher-student architecture. To extract the information of the OOD unlabeled images, our method leverages the consistency constrains of both spatial feature representation and the probability learned from teacher model and student model. The performance and generalization of the proposed method are evaluated on the public databases including Messidor, REFUGE and iChallenge-AMD. The experimental results show that the proposed approach outperforms the existing semi-supervised methods.
An Obstacle Detection Technique for Marine Intelligent Transportation System based on Maximum likelihood Method

Obstacle detection is a key technology for marine intelligent transportation system. In this paper, an obstacle detection technique for marine intelligent transportation system based on Maximum Likelihood (ML) processor is proposed. Different from most of the former source localization algorithms, our proposed algorithm explicitly makes use of both the horizontal L-Shaped array and the underwater acoustic propagation model to achieve a relatively high spatial resolution. Numerical simulations are presented to demonstrate the superior performance of our proposed algorithm.
Dual-Branch Attention Network and Swin Spatial Pyramid Pooling for Retinopathy of Prematurity Classification

Retinopathy of prematurity (ROP) is an abnormal proliferative vascular disorder that causes blindness and vision impairment in premature children. Early diagnosis of ROP is crucial in reducing the blindness rate. However, traditional convolutional neural networks (CNNs) fail to optimize the global information of pathology images, while the recent popular vision transformer (ViT) model neglects the local details. Also, ROP has a small lesion region and relatively little data. To solve above issues, we propose a dual-branch network, which is a parallel structure composed of ResNet-50 and multi-axis vision transformer (MaxViT) for capturing local and global information about fundus images simultaneously. Multi-scale features are extracted using the Swin Spatial Pyramid Pooling (Swin-SPP) operation in SwinViT blocks. The convolutional block attention module (CBAM) is utilized to highlight the channel and spatial relationships of ROP staging features. The experimental results on internal ROP dataset indicate that our method shows promising for ROP staging.
Rethinking Fall Detection With Wi-Fi

The past decades have witnessed a surge in human fall detection with sensors, cameras, and wireless signals. Among them, Wi-Fi-based fall detection has been one of the most attractive solutions due to the ubiquitous and pervasive deployment of Wi-Fi infrastructures. However, these approaches are still difficult to be put into practical use. To push forward Wi-Fi-based fall detection for wide deployment, three major limitations concerning environmental diversity, motion diversity, and user diversity are required to be resolved. In this paper, we propose FallDar, a Wi-Fi-based deep learning-assisted fall detection system that outperforms state-of-the-art works on the three criteria simultaneously. First, to deal with environmental diversity, FallDar characterizes falls with the speed of the body, which is the most relevant and inherent feature of falling activities, making the system resilient to environmental changes. Second, to deal with motion diversity, FallDar simulates a large amount of fall data of various falling types with a DNN-based generative model. Training with these data, FallDar is endowed the capability of detecting more types of falls. Third, to deal with user diversity, FallDar proposes to incorporate the fall detection network with a user identification network. The network is designed to extract user-independent features, requiring no fall data from new users for system adjustment. We implement FallDar on commercial Wi-Fi devices and conduct experiments in home and office environments for six months. The evaluation results show that FallDar achieves a false alarm rate of 5.7% and a missed alarm rate of 3.4% across all factors, making a fundamental step towards ubiquitous fall detection with Wi-Fi.
Foveal Avascular Zone Segmentation Using Deep Learning-Driven Image-Level Optimization and Fundus Photographs

The foveal avascular zone (FAZ) is a retinal area devoid of capillaries and associated with multiple retinal pathologies and visual acuity. Optical Coherence Tomography Angiography (OCT-A) is a very effective means of visualizing retinal vascular and avascular areas, but its use remains limited to research settings due to its complex optics limiting availability. On the other hand, fundus photography is widely available and often adopted in population studies. In this work, we test the feasibility of estimating the FAZ from fundus photos using three different approaches. The first two approaches rely on pixel-level and image-level FAZ information to segment FAZ pixels and regress FAZ area, respectively. The third is a training mask-free pipeline combining saliency maps with an active contours approach to segment FAZ pixels while being trained on image-level measures of the FAZ areas. This enables training FAZ segmentation methods without manual alignment of fundus and OCT-A images, a time-consuming process, which limits the dataset that can be used for training. Segmentation methods trained on pixel-level labels and image-level labels had good agreement with masks from a human grader (respectively DICE of 0.45 and 0.4). Results indicate the feasibility of using fundus images as a proxy to estimate the FAZ when angiography data is not available.
Research of Cell-Decomposition in Polar Coordinate System for Point Cloud Semantic Segmentation

In this paper, a polar-coordinate-based PointNet++ is proposed to classify objects such as roads, cars, motorcycles, and pedestrians using point clouds of outdoor scenes captured by LiDAR. Since the outdoor point clouds obtained by LiDAR are sparse in distance, if the point clouds are partitioned by a grid of Cartesian coordinates, the number of points in each partition varies greatly, which causes the bias of the learning network. With the SemanticKITTI data set, the experimental results of polar-coordinate-based PointNet++ show that the proposed method can improve the overall accuracy and processing speed. The results show that Mean Intersection over Union (mIoU) is 52% and overall accuracy (oAcc) is 78%, processing speed per frame improve from 300ms to 30ms.
An Efficient and Secure DAG-based LoRaWAN System

The conventional LoRa system faces the challenges of security and privacy due to centralized architecture and transparent forwarding mechanism. To address these challenges, a Directed Acyclic Graph (DAG)based LoRaWAN system is proposed in this paper. The proposed system is designed with distributed LoRaWAN architecture. Distributed LoRa gateways and network servers record data transmissions in tangle network to make LoRa data traceable and avoid single point of failure. In addition, a secure LoRa data ledger (SLDL) using digital signature algorithm and symmetric-key encryption is designed to defend against malicious gateway attacks, eavesdropping, etc. The proposed system is implemented and validated using DAG-based IOTA platform. The results demonstrate that the prototype system can achieve nearly up to 100 transaction per second (TPS) of throughput and about 1.3 second latency, which is about 90% reduction than existing blockchain-based solutions. As a result, the proposed DAG-based LoRaWAN system is an effective solution to ensure the security and efficiency of large-scale LoRa networks.
A Compliance Control Strategy for an Upper-Limb Rehabilitation Robot with a Quick and Real-Time Human-Robot Interactive Force Estimation

In order to improve the safety of rehabilitation training and estimate physical human-robot interaction(pHRI) force without force sensor, this paper designs a real-time interactive force learning controller in an End-Effector Type Robot. The adaptive sliding mode term in controller is used to solve the uncertainties in the robot modeling and inertial parameter measurements. The learning law term can estimate pHRI force. To investigate the performance of this controller, experiments were designed to repeat the tracking task on a circular trajectory. The result shows that this controller can make the tracking error and the estimate error of pHRI force gradually acquire a desired stability margin. The error of resultant force of pHRI between the estimating and the measuring by the force sensor is 2.87±2.50N. This controller enables the robot to exhibit high compliance and its estimation of the pHRI force takes less time and is more accurate.
Deep Learning Approach to Predict Alzheimer's Disease through Magnetic Resonance Images

Alzheimer's disease is the most common type of dementia that causes many of the functions of the human brain to be severely weakened. To date, there has not been a cure for Alzheimer's disease. Therefore, early diagnosis is needed using MRI images with the help of a classification program. Deep learning using the Convolutional Neural Network (CNN) method is receiving increasing attention because of its excellent performance. Its architecture can be modified according to user needs based on the data to be processed and the approach for classifying, detecting, and segmenting visual objects. In this paper, we offer a classification of Alzheimer's disease using one of the architectures on CNN, namely Visual Geometry Group-19 (VGG-19), with a sagittal view of MRI images with an image size of 229 x 229 pixels. The classification accuracy of the described method is 94% for the validation set.
Evaluating virtual staining for high-throughput screening

Little is known about the feasibility of virtual staining for industry applications such as high-throughput screening (HTS). We provide a thorough analysis of the usability of image-to-image translation for the virtual staining of label-free bright-field microscopy images of live cells, using a pool of more than 1.6 million images across six lung, six ovarian and six breast cell lines consisting of paired bright-field, cytoplasm, nuclei and DNA-damage stains. To our knowledge this is the first time an analysis of virtual staining has been performed on three levels; pixel-based, biological-feature based, and determining if virtual staining can reproduce drug-effect. Our results reveal that while virtually stained nuclei and cytoplasm images often consistently and faithfully reproduce the information found in fluorescence microscopy, virtually stained images of DNA-damage are usually less accurate.
A Consistency Preservation Method for Content Sharing in Peer-to-Peer Network Using CCN Control

This paper proposes a content-sharing method to operate pure P2P at the lower network load by porting the functions in the CCN router that are operated in the network layer to the application layer where the conventional P2P works. Furthermore, the consistency control function using the timestamp of the content updates is added. The effectiveness of the proposed method is evaluated using computer simulations.
Design of a Wideband SIW Slot Antenna with Enhanced Gain

This article demonstrates a wideband SIW slot antenna with gain-enhancing capability. The upper layer of SIW is composed of a 50 Ω microstrip line and radiation slots. The radiation slots are composed of a multiple branch slot and a couple of oblique slots. The multiple branch slot can excite triple resonant modes for extending impedance matching bandwidth and gain, while the oblique slots are utilized to match the microwave line so that further enhancing the impedance matching bandwidth. Simulated results of the SIW slot antenna indicate that the relative bandwidth is up to 16.7%. The gain at 9.0 GHz reaches 9.3 dBi, while the 1-dB gain bandwidth reaches 25.5%.
Childhood Functional Gastrointestinal Disorders : Child / Adolescent

Division of Digestive Diseases, Hepatology, and Nutrition, Connecticut Children’s Medical Center, Hartford, Connecticut; Division of Digestive Diseases, Hepatology, and Nutrition, Nationwide Children’s Hospital, Columbus, Ohio; Baylor College of Medicine, Children’s Nutrition Research Center, Texas Children’s Hospital, Houston, Texas; Department of Translational Science, Section of Pediatrics, University of Naples, Federico II, Naples, Italy; and Department of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
Stressful Life Events Stressful life events are associated with symptom xacerbation among adults with IBS

ONA L. LEVY,* KEVIN W. OLDEN, BRUCE D. NALIBOFF, LAURENCE A. BRADLEY, ARLOS FRANCISCONI, DOUGLAS A. DROSSMAN,** and FRANCIS CREED University of Washington, Seattle, Washington; University of South Alabama, Mobile, Alabama; University of California at Los Angeles, os Angeles, California; University of Alabama at Birmingham, Birmingham, Alabama; Hospital de Clinicas de Porto, Porto Alegre, Brazil; *University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; and University of Manchester, Manchester, England