Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-Based Target Localization with Cooperative UAVs Towards Indoor Surveillance

Unmanned aerial vehicles (UAVs) have been widely adopted for a variety of civilian and military applications. Despite their many advantages, most UAVs are not suitable for indoor missions due to the lack of global position system (GPS) and the large size of UAV with diverse sensors. In this work, a lightweight and cost-effective indoor multi-UAV surveillance system is presented for accurate target localization. The proposed system employs a vision-based architecture, leveraging ORB-SLAM for self-localization and YOLOv3 for object detection. The triangulation method is employed for target positioning, offering reliable performance with negligible time delays and improved detection compared to depth camera approaches. After that, the Riccati observer is introduced to address the stochastic nature of the system. Through a series of experiments involving real UAVs, the system demonstrates its ability to accurately localize and track targets in indoor environments, updating the UAVs’ positions in real-time with optimal performance.
Characterization of Leptin Secretion in Premenopausal Obese Women Treated with Bromocriptine

Leptin, a hormone secreted by adipose tissue, is primarily responsible for inhibiting hunger and maintaining energy balance. Improper leptin secretion may result in hyperleptinemia (excess secretion of leptin) or leptin resistance, both of which contribute to obesity. Diagnosing abnormal leptin secretion may help treat this underlying cause of obesity. Therefore, continuous monitoring of the level of leptin may help characterize its secretion dynamics and also help devise an appropriate treatment. In this research, we consider leptin hormone concentration data taken over a 24 hour time period from eighteen healthy premenopausal obese women before and after treatment with a dopamine agonist, bromocriptine, and deconvolve the observed leptin hormone levels to estimate the number, timing, and magnitude of the underlying leptin secretory pulses. We find that there is an overall decrease in leptin secretion, particularly during sleep, but the changes in the secretory and clearance rates, and the number of pulses underlying the secretion process are not statistically significant.Clinical relevance— This work seeks to understand the effect of bromocriptine on leptin secretory dynamics and will help further current understanding of the effect of bromocriptine in relation to obesity.
Cellsketch: Simplified Cell Representation for Label-free Cell and Nuclei Segmentation

This paper presents a novel technique for cell segmentation, named "Cellsketch," which generates an RGB mask containing simplified representations of cells (including nuclei, whole-cell, and cell boundaries) from microscopic images, and applies the watershed algorithm to produce segmentation masks of cells and nuclei. The RGB mask is generated using a generator model trained with a combination of L1 loss and adversarial loss. The method achieved accurate cell and nuclei segmentation from differential interference contrast (DIC) images using only automatically annotated training data and shows potential for a generalizable algorithm for cell segmentation. The code is freely available at: https://github.com/iranovianti/cellsketchClinical Relevance— This method simultaneously detects individual cells and nuclei from DIC images.
Geometrically Consistent Monocular Metric-Semantic 3D Mapping for Indoor Environments with Transparent and Reflecting Objects

3D mapping is crucial for many applications in robotics and related industries. To build dense high-quality point clouds accurate depth estimation or completion is needed. This paper presents the development of a metric-semantic mapping pipeline based on Deep Neural Networks (DNN) which assures geometrical consistency with enhancements for chal-lenging environments with transparent and reflecting objects like glass walls, doors, and mirrors. The suggested approach uses camera ego-motion alongside its sparse visual features to avoid the scale ambiguity issue caused by monocular depth affine-invariant estimations and to able to restore metric consistent depth information. Visual-inertial odometry data is used for camera pose graph optimization with no need to use RGB-D cameras. The proposed pipeline incorporates semantic segmentation and robust filtering to refine point clouds by removing outliers associated with mirrors and glass surfaces. Latency-aware performance and quality evaluation of 3D scene reconstruction were carried out on both a specially prepared dataset that reflects office-like scenes with multiple transparent objects and a public ScanNet dataset. The quantitative and qualitative results show that the proposed solution outperforms other state-of-art DNN-based models and algorithms as well as RGB-D cameras in terms of metric depth geometric consistency, 3D reconstruction accuracy, and the ability to preserve mesh quality in challenging scenarios with transparent and reflective surfaces.
De-lighting Human Images Using Region-Specific Data Augmentation

This study aims to use deep learning to recover the diffuse albedo of human images captured under a wide range of real-world lighting conditions. A key challenge here is the wide variety of textures found in full-body human images. While some aspects like skin color have a limited color range, clothing and accessories display a broad spectrum of colors and textures. As a result, creating a comprehensive dataset with accurate labels is unfeasible. To address this, we propose a data augmentation method that involves applying color-shifts to various semantic regions within our training images, all while maintaining realistic appearance. This process is accomplished by initially segmenting the ground-truth albedos into their respective components (e.g., pants, shirt, hair, etc.) using a pre-trained human parsing network. Then, we adjust their hue and intensity channels using randomly chosen values from a carefully defined distribution. Our results show significant improvements in albedo recovery, especially in clothing areas, and better performance with underrepresented skin tones.
Face-Free Chest Detection Using Convolutional Neural Networks for Non-Contact Respiration Monitoring

Non-contact methods for monitoring respiration face limitations when it comes to selecting the chest region of interest. The semi-automatic method, which requires the user to select the chest region in the first frame, is not suitable for real-time applications. The automatic method, which tracks the face first and then detects the chest region based on the face’s position, can be inaccurate if the face is not visible or is rotated. Moreover, using the face region to track the chest region can under-utilize camera pixels since the face is not essential for monitoring respiration. This approach may adversely affect the quality of the respiration signal being measured. To address these issues, we propose a face-free chest detection model based on Convolutional Neural Networks. Our model enhances the measured non-contact respiration signal quality and utilizes more pixels for the chest region alone. In our quantitative study, we demonstrate that our method outperforms traditional methods that require the presence of the face. This approach offers potential benefits for real-time, non-contact respiration monitoring applicationsClinical relevance— This work enhances the performance of non-contact respiration monitoring techniques by precisely detecting the chest region without the need of face in it through a CNN-based model. The use of the CNN-based chest detection model also enhances the real-time monitoring capabilities of non-contact respiration monitoring techniques.
Analysis and Removal of Acoustic Rolling Shutter Effect

As the demand for ocean observations increases, the quality requirements for sonar imaging are becoming increasingly exacting. The temporal synthesis strategy provides the acoustic-lens-based forward-looking sonar with ultra-high resolution, but also causes the imaging content to be distorted when the sonar is moving at high speed. This limits the application of sonar to low-speed or stationary underwater inspections. In this paper, we define this distortion effect formally for the first time as an acoustic rolling shutter effect, and use implicit neural representation to naturally recover and represent acoustic images in a rectangular coordinate system for visual representation or subsequent computer tasks, e.g., image registration and image mosaicing. The method is self-supervised and involves only a single image, without the need for any external data input, e.g. global positioning system and doppler velocity log. We present qualitative results to demonstrate the feasibility of the proposed method through field experimental data.
Joint Active and Passive Beamforming Design in RIS-Aided Cell-Free Massive MIMO Systems for Aerial Networks

Multiple-input multiple-output (MIMO) communication system and reconfigurable intelligent surface (RIS) are widely accepted as two promising approaches towards the secure data transmission in 6G networks, including the aerial part. In this paper, we jointly research one RIS-aided cell-free massive MIMO system for 6G aerial networks where one RIS is deployed around access points (APs) and users. By deploying one RIS in cell-free MIMO system, we can create favorable propagation conditions in a low-cost way. We first formulate the max-min fairness problem in order to maximize the achievable rate among all the users. In order to tackle this problem, we propose a joint design framework for the transmit beamforming at APs and the phase shifts at RIS by alternating optimization. In the process of alternating optimization, the transmit beamfoming design (active beamforming) problem can be solved by a second-order-cone program (SOCP), the phase shifts design (passive beamforming) problem can be transformed into a semidefinite program (SDP) making use of a semidefinite relaxation (SDR). In addition, we propose an approximation projection for the discrete phase shifts design problem. The simulation results reveal that the proposed scheme brings a significant performance gain to the cell-free MIMO systems powered by an RIS compared with the case of no RIS.
Cell Nuclei Segmentation With Dynamic Token-Based Attention Network

Cell nuclei segmentation is crucial for analyzing cell structure in different tasks, i.e., cell identification, classification, etc., to treat various diseases. Several convolutional neural network-based architectures have been proposed for segmenting cell nuclei. Although these methods show superior performance, they lack the ability to predict reliable masks when using biomedical image data. This paper proposes a novel Dynamic Token-based Attention Network (DTA-Net). Combining convolutional neural networks (CNN) with a vision transformer (ViT) allows us to capture detailed spatial information from images efficiently by encoding local and global features. Dynamic Token-based Attention (DTA) module calculates attention maps keeping the overall computational and training costs minimal. For the nuclei segmentation task on the 2018 Science Bowl dataset, our proposed method outperformed SOTA networks with the highest Dice similarity score (DSC) of 93.02% and Intersection over Union (IoU) of 87.91% without using image pre- or post-processing techniques. The results showed that high-quality segmentation masks could be obtained by configuring a ViT in the most straight forward manner.Clinical relevance— In this work, the segmentation of cell nuclei in microscopy images is carried out automatically, irrespective of their appearance, density, magnification, illumination, and modality
Simulation of Graphene-based Materials and Devices for Sub-Terahertz Applications

The electromagnetic properties and behavior of nanomaterials and nanomaterial-based devices are simulated using analytical models and numerical methods. Graphene nanomaterials are the most explored, with several different proposed applications. To design a graphene-based device that meets specific functional requirements, reliable models that take into account the geometry of the device, in addition to the properties of graphene. In this way, simulation of the response based on variations in geometrical parameters is possible. Among the existing techniques, finite element method for field analysis and device compact modeling for circuit simulators are here referred. In this paper, our design approach is presented, which begins with metamaterial and device modeling based on experimental data fitting and progresses to sensitivity analysis conducted using a factorial design of numerical experiments.
Automated Myocardial Infarction Screening Using Morphology-Based Electrocardiogram Biomarkers

Ischemic heart disease (IHD), a critical and dreadful cardiovascular disease, is a leading cause of death globally. The steady progress of IHD leads to an irreversible condition called myocardial infarction (MI). The detection of MI can be done by observing the altered electrocardiogram (ECG) characteristics. Often, automated ECG analysis is preferred in place of visual inspection to reduce time and ensure reliable detection even when the recording quality is not very good. This paper presents an automated approach to classify recent MI, past MI, and normal sinus rhythm (NSR) classes based on the morphological features of the ECG. In clinical practice, a standard 12-lead ECG setup is typically employed to identify MI. However, acquiring a 12-lead ECG is not always convenient. Hence, in this study, we have explored the possibility of using a minimal number of ECG leads by deriving the augmented limb leads using leads I and II. A well-known and widely used ensemble machine learning tool, the random forest (RF) classifier is trained using features extracted from the derived augmented limb leads and their combinations. An RF classifier built using features extracted from all limb leads has outperformed classifiers built on combinations of them with five-fold cross-validation training accuracy of 97.9 (±0.008) % and testing accuracy of 98 %. Clinical relevance— As high sensitivity is reported in identifying recent MI and past MI classes, the proposed approach is suitable for preventative healthcare applications since it is less likely that subjects with recent or past MI will be misclassified. Due to its low computational complexity, better interpretability, and comparable performance to the state-of-the-art results, the proposed approach can be employed in clinical and cardiac health screening applications. It also has the potential to be employed in remote monitoring with mobile and wearable devices because it is built on features extracted from only lead I and II ECG recordings.
A Soft, Multi-Layer, Kirigami Inspired Robotic Gripper with a Compact, Compression-Based Actuation System

Over the last decade, a plethora of soft robotic devices have been proposed for the execution of complex grasping and dexterous manipulation tasks. Tasks requiring such increased dexterity are typically executed using fully-actuated, rigid end-effectors equipped with sophisticated sensing and controlled with complex control laws. The new class of soft robotic devices offers an alternative to the traditional end-effectors and facilitates the development of robotic grasping and manipulation solutions that are lightweight, safe to interact with, affordable, and easy to use and control. Within the class of soft robotic grippers and hands, promising recent developments were made in ultra-affordable, even disposable mechanisms based on origami and kirigami structures. This paper proposes a new kirigami-inspired robotic gripper geometry employing compression-based actuation. The compression actuation fundamentally differentiates this new design class from previous kirigami grippers, resulting in more compact robotic grippers with superior grasping capabilities. In particular, we investigate how the shapes of the internal cuts of the kirigami geometries can affect the gripper performance in terms of force exertion and grasping capabilities. A series of experiments are conducted to understand better the working principles behind this new type of kirigami grippers and experimentally validate their efficacy in the execution of complex, everyday life tasks. Further demonstrations of the gripper's capabilities include the pick-and-placing of human hair, egg yolk, and even liquids.
A Novel Form Factor For PPG-based Blood Pressure Monitoring

Blood pressure (BP) is one of the four main vital signs in medicine and may be a useful signal for wellness tracking and for user-aware interfaces in human-computer interaction. The current standard for BP measurement uses cuff-based devices that block an artery temporarily to get a single, discrete measurement of BP. Recently, there have been significant efforts to measure correlates of BP continuously and non-invasively from relevant signals like photoplethysmography (PPG), which responds to volumetric changes in arteries due to blood pulsations. In this paper, we explore a novel setup with two points of instrumentation, one on the head and a second on the wrist, for recording PPG and estimating the pulse wave velocity, which is a major correlate of BP, along with other waveform-related features. We prospectively tested the device on 10 subjects who followed a protocol for the deliberate variation of BP while ground truth measurements were taken using a reference cuff-device. Generic absolute BP models, which use the collected data for leave-one-subject-out cross-validation, yielded an error of −0.14 ± 7.3 mmHg for systolic BP (SBP) and −0.21±6.7 mmHg for diastolic BP (DBP), which are within the regulatory limits of 5 ± 8 mmHg. Notably, the correlation between the predicted BPs and the ground truth BPs was higher for SBP (r = 0.74, p < 0.001) than for DBP (r = 0.34, p < 0.001). The results show that the proposed form factor can extract BP-related features that could be used for continuous, cuff-less BP monitoring.
Hand Force Estimation from Acoustic Myography Using Deep Wavelet Scattering Transform and Long Short-Term Memory

The ability to estimate user intention from surface electromyogram (sEMG) signals is a crucial aspect in the design of powered prosthetics. Recently, researchers have been using regression techniques to connect the user's intent, as expressed through sEMG signals, to the force applied at the fingertips in order to achieve a natural and accurate form of control. However, there are still challenges associated with processing sEMG signals that need to be overcome to allow for widespread and clinical implementation of upper limb prostheses. As a result, alternative modalities functioning as promising control signals have been proposed as source of control input rather than the sEMG, such as Acoustic Myography (AMG). In this study, six high sensitivity array microphones were used to acquire AMG signals, with custom-built 3D printed microphone housing. To tackle the challenge of extracting the relevant information from AMG signals, the Wavelet Scattering Transform (WST) was utilized. alongside a Long Short-Term Memory (LSTM) neural network model for predicting the force from the AMG. The subjects were asked to use a hand dynamometer to measure the changes in force and correlate that to the force predicted by using the AMG features. Seven subjects were recruited for data collection in this study, using hardware designed by the research team. the performance results showed that the WST-LSTM model can be robustly utilized across varying window sizes and testing schemes, to achieve average NRMSE results of approximately 8%. These pioneering results suggest that AMG signals can be utilized to reliably estimate the force levels that the muscles are applying.Clinical Relevance— This research presents a new method for controlling upper limb prostheses using Acoustic Myography (AMG) signals. A novel method mapping the AMG signals to force applied by the corresponding muscles is developed. The presented findings have the potential to lead to the development of more natural and accurate control of human-machine interfaces.
ANEC: Adaptive Neural Ensemble Controller for Mitigating Latency Problems in Vision-Based Autonomous Driving

Humans have latency in their visual perception system between observation and action. Any action we take is based on an earlier observation since, by the time we act, the state has already changed, and we got a new observation. In autonomous driving, this latency is also present, determined by the amount of time the control algorithm needs to process information before acting. This algorithmic perception latency can be reduced by massive computing power via GPUs and FPGAs, which is improbable in automobile platforms. Thus, it is a reasonable assumption that the algorithmic perception latency is inevitable. Many researchers have developed different neural network driving models without consideration of the algorithmic perception latency. This paper studies the latency effect on vision-based neural network autonomous driving in the lane-keeping task and proposes a vision-based novel neural network controller, the Adaptive Neural Ensemble Controller (ANEC) that is inspired by the near/far gaze distribution of human drivers during lane-keeping. ANEC was tested in Gazebo 3D simulation environment with Robot Operating System (ROS) which showed the effectiveness of ANEC in dealing with algorithmic latency. The source code is available at https://github.com/jrkwon/oscar/tree/devel_anec.
Deep Learning Networks for Breast Lesion Classification in Ultrasound Images: A Comparative Study

Accurate lesion classification as benign or malignant in breast ultrasound (BUS) images is a critical task that requires experienced radiologists and has many challenges, such as poor image quality, artifacts, and high lesion variability. Thus, automatic lesion classification may aid professionals in breast cancer diagnosis. In this scope, computer-aided diagnosis systems have been proposed to assist in medical image interpretation, outperforming the intra and inter-observer variability. Recently, such systems using convolutional neural networks have demonstrated impressive results in medical image classification tasks. However, the lack of public benchmarks and a standardized evaluation method hampers the performance comparison of networks. This work is a benchmark for lesion classification in BUS images comparing six state-of-the-art networks: GoogLeNet, InceptionV3, ResNet, DenseNet, MobileNetV2, and EfficientNet. For each network, five input data variations that include segmentation information were tested to compare their impact on the final performance. The methods were trained on a multi-center BUS dataset (BUSI and UDIAT) and evaluated using the following metrics: precision, sensitivity, F1-score, accuracy, and area under the curve (AUC). Overall, the lesion with a thin border of background provides the best performance. For this input data, EfficientNet obtained the best results: an accuracy of 97.65% and an AUC of 96.30%. Clinical Relevance— This study showed the potential of deep neural networks to be used in clinical practice for breast lesion classification, also suggesting the best model choices.