Miracle-dz/UniFAR-FTUs huggingface dataset:
Hardware-in-the-Loop Simulation of Vehicle-Manipulator Systems for Physical Interaction Tasks

Hardware-in-the-loop simulation (HILS) allows a more realistic evaluation of control approaches than what is possible with pure software simulations, but without the actual complexity of the complete system. This is important for some complex systems such as orbital robots, where testing of the system is typically not possible after its launch, and an on-ground replica is used to validate the performance of such a system. In this article, an impedance-matching approach is presented to match the end-effector dynamics of a fixed-base robot manipulator with that of a target vehicle-manipulator system (VMS), while taking into account the redundant nullspace dynamics in a connected real-time simulation framework. This approach ensures that the forces and torques exerted by the system on the environment matches with that of the simulated system. The contact wrenches used in our approach are not obtained from numerical simulations, but rather from real physical interaction, which is one of the main advantages of our approach. The effectiveness of our method is validated by demonstrating various physical interaction tasks with the environment, using a suspended aerial manipulator as the target system.
A Novel 3D Beamforming Based Initial Access Procedure Design for Satellite IoT

Aiming at providing communication services to sparsely-populated areas and easing the burden of the existing ground network, non-terrestrial networks (NTN) have been under the exploration of 3GPP. In this present research, a novel design of the initial access procedure is proposed on the premise that both the satellite base station (SBS) and the terrestrial base station (TBS) are capable of leveraging large antenna arrays to perform beamforming at both transmission and receiving ends. In the proposed architecture, a group of user equipment (UE) can share the same resource for communication with other groups of UE within the same satellite cell, in light of the spatial orthogonality this designed system promises. Analysis and simulations not only reveal that in this architecture, a desirable signal-to-interference-plus-noise ratio (SINR) can be obtained if a proper searching beam offset is chosen, but also infer a 99% preamble detection rate can be reached even within a very low SINR range in single beam scenario.
A Novel Wideband Low Profile Fabry-Perot Cavity Antenna Using Single-Layer Partially Reflective Surface

In this paper, we present a design for improving the bandwidth of Fabry-Perot cavity antenna (FPCA) employing a single layer partially reflective surface (PRS). A novel two-sided unit cell is suggested for PRS which is composed of a square patch and an H-shaped ring to create a positive reflection phase gradient. The designed antenna works in the x-band and for its feeding source, an aperture-coupled microstrip antenna is utilized. The single layer of PRS enables the advantage of a low profile along with its wide bandwidth. According to the simulation results, a 3dB gain bandwidth of 34.24 % is obtained for the antenna in the frequency range of 8.21-11.6 GHz and an impedance bandwidth (S11<-10dB) of 50.17% in the frequency range of 8.72 -14.56 GHz.
Evidence for Variscan dextral transpression in the Pilton Shales, Croyde Bay, North Devon

Structures in the Devonian-Carboniferous Pilton Shales on the north and south sides of Croyde Bay (Figure 1) display features which can be put together in a unifying model of dextral transpression. The multilayered slates and thin siltstones are tightly folded into subhorizontal angular upright folds with interlimb angles of between 90 and 60°. Late dextral shear was superimposed upon the already developed folds generating east-south-east-trending dextral vein arrays, conjugate southsouth-east sinistral vein arrays and an anticlockwise transecting spaced cleavage (Figure 2).
Deep learning based reconstruction enables high-resolution electrical impedance tomography for lung function assessment

Recently, deep learning based methods have shown potential as alternative approaches for lung time difference electrical impedance tomography (tdEIT) reconstruction other than traditional regularized least square methods, that have inherent severe ill-posedness and low spatial resolution posing challenges for further interpretation. However, the validation of deep learning reconstruction quality is mainly focused on simulated data rather than in vivo human chest data, and on image quality rather than clinical indicator accuracy. In this study, a variational autoencoder is trained on high-resolution human chest simulations, and inference results on an EIT dataset collected from 22 healthy subjects performing various breathing paradigms are benchmarked with simultaneous spirometry measurements. The deep learning reconstructed global conductivity is significantly correlated with measured volume-time curves with correlation > 0.9. EIT lung function indicators from the reconstruction are also highly correlated with standard spirometry indicators with correlation > 0.75. Clinical Relevance— Our deep learning reconstruction method of lung tdEIT can predict lung volume and spirometry indicators while generating high-resolution EIT images, revealing potential of being a competitive approach in clinical settings.
Pillar Centroid Tracker for the Measurement of Protrusive Forces Generated by Hyphal Microorganisms

Elastomeric micropillars constitute a versatile sensor platform to investigate the forces exerted by microorganisms. Microfabricated to match the scale of the target organisms, the force imparted on a sensor pillar leads to a deflection of the pillar top, which can be recorded using optical microscopy. Knowing the height of the contact point, pillar dimensions and material properties, the deflection can be easily converted into a force value based on a simple bending and shear model for a cylinder. However, current software approaches developed to perform the pillar top tracking and conversion lack in automation and user-friendliness. In this paper we present a pillar centroid tracker (PCT) software tool which combines a specialised pillar tracking algorithm with general improvements to data processing steps, integrating all parts of the workflow into a single, streamlined application. We compare performance of the tool to existing approaches and showcase its operation by applying it to the example of protrusive force sensing in hyphal microorganisms.
Ultra-Wideband MIMO Antennas: Latest Advances in Design and Technology

The exponential growth of digital platforms and the increasing user base in wireless communication has created a need for communication systems that can provide high data speeds, reliability, and strong transmission links. The limitations of single antenna systems have led to an increased focus on multiple-input multiple-output (MIMO) antennas in order to address the requirements of modern high-speed wireless systems. Nevertheless, the attainment of adequate isolation in compact 5G terminals presents certain obstacles. The primary objective of this review paper is to examine the fundamental aspects, performance metrics, and design methodologies pertaining to multiple-input multiple-output (MIMO) antennas. More specifically, the paper delves into the characteristics of dual-band, ultra-wideband (UWB), and circularly polarized antennas. The paper investigates several aspects of present MIMO antenna design procedures, including channel capacity loss (CCL), envelope correlation coefficient (ECC), gain, efficiency, and isolation approaches, in order to facilitate a comparative analysis. This research makes a valuable contribution to the progress of high-speed wireless communication systems by thoroughly understanding the merits and constraints of each strategy. The implementation of Multiple-Input Multiple-Output (MIMO) antennas shows potential for various wireless applications, encompassing portable devices, smart gadgets, automobiles, and wireless sensors.
Concept and Design of a Prototype Autonomous, Modular Subsea Bottom Station

This paper deals with the introduction of a novel concept of a customisable, modular and autonomous subsea bottom station (BASE). The subsea station will be supported by a floating surface unit ensuring cloud data connection as well as including MEMS-based energy harvesting and spaces for additional sensors. The concept not only serves as a long-term survey station or lander in the classical sense. It shall also operate as the main hub for a future underwater communications network and as a service station for underwater vehicles, such as AUVs, operating in the area. This paper aims to introduce the overall system and to showcase concepts of selected subsystems.
Real-Time Pose Estimation of Rats Based on Stereo Vision Embedded in a Robotic Rat

In this paper, we propose a system for real-time rat pose estimation based on stereo vision. The system is dedicated to robot-rat interaction research. First, we design a lightweight, high-resolution network (RRKDNet) for keypoint detection of the rat. The network is trained on a dataset of rat images, which are captured by the robotic rat in first-person view. Second, based on the keypoint detection results, the pose of the rat is obtained by stereo vision model calculation and robot coordinate transformation. At last, we complete a real-time simulation experiment to reproduce the pose of the rat and the robotic rat. The system has been subjected to a series of experiments and the results demonstrate that our network performs better in speed and performance than similar networks. Compared to similar networks, our network has about one-third the number of parameters, while the detection rate increases by 45.25% (the detection rate is 71.57%). The inference speed (34.42 FPS with dual model simultaneous inference) is also faster. The validation error is only 13.85 pixels on the homemade dataset, which is lower than all backbones in Deeplabcut (a toolbox more frequently used for rat keypoint detection). Thus, this work is a significant step in the autonomous intelligent interaction between robots and rats.
The Performance Improvement of Ultrasound Localization Microscopy (ULM) Using the Robust Principal Component Analysis (RPCA)

This paper presents an algorithm for ultrafast ultrasound localization microscopy (ULM) used for the detection, localization, accumulation, and rendering of intravenously injected ultrasound contrast agents (UCAs) enabling to yield hemodynamic maps of the brain microvasculature. It consists in integrating a robust principal component analysis (RPCA)-based approach into the ULM process for more robust tissue filtering, resulting in more accurate ULM images. Numerical experiments conducted on an in vivo rat brain perfusion dataset demonstrate the efficiency of the proposed approach compared to the most widely used state-of-the-art method.
Alzheimer’s Together with Mild Cognitive Impairment Screening Using Polar Transformation of Middle Zone of Fundus Images Based Deep Learning

Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) are considered an increasing major health problem in elderlies. However, current clinical methods of Alzheimer’s detection are expensive and difficult to access, making the detection inconvenient and unsuitable for developing countries such as Thailand. Thus, we developed a method of AD together with MCI screening by fine-tuning a pre-trained Densely Connected Convolutional Network (DenseNet-121) model using the middle zone of polar transformed fundus image. The polar transformation in the middle zone of the fundus is a key factor helping the model to extract features more effectively and that enhances the model accuracy. The dataset was divided into 2 groups: normal and abnormal (AD and MCI). This method can classify between normal and abnormal patients with 96% accuracy, 99% sensitivity, 90% specificity, 95% precision, and 97% F1 score. Parts of both MCI and AD input images that most impact the classification score visualized by Grad-CAM++ focus in superior and inferior retinal quadrants.Clinical relevance– The parts of both MCI and AD input images that have the most impact the classification score (visualized by Grad-CAM++) are superior and inferior retinal quadrants. Polar transformation of the middle zone of retinal fundus images is a key factor that enhances the classification accuracy.
Skirting Line Estimation Using Sparse to Dense Deformation

Automating the process of fleece contaminant removal has the potential to drastically improve the quality of wool leaving the farm gate. Towards this goal, we present a method to automatically extract skirting lines, i.e., the separations between clean and contaminated wool of a fleece using RGB images. We propose a learning-based sparse-to-dense approach for estimating the non-rigid deformation of fleeces in order to estimate the skirting lines. Our method is bootstrapped from a set of sparse inlier feature correspon-dences, which are heavily filtered through a set of strict criteria. The inlier correspondences are then greedily expanded by adding correspondences from a denser set through a filtering process. This process is based on a learning approach that takes as inputs the pixel similarity and the consistency with their inlier neighbours. Each greedy iteration is initialised with a non-rigid deformation using as-rigid-as-possible as a prior to the filtering process. The proposed method outperforms both a rigid deformation baseline and optic flow deep learning approach, as evidenced by the quantitative evaluation of pixel location error in controlled experiments. To further prove its practicality, we demonstrate qualitative results comparing the predicted skirting line from various methods on images of skirted fleeces collected from several wool sheds.
Comparing the Effectiveness of Control Methodologies of a Hip-Knee-Ankle Exoskeleton During Squatting

Manual materials handling occupations often involve repetitive lifting, lowering, and carrying motions, which can lead to muscular fatigue and/or injury. The risk increases when loads must be worn on the body for the entirety of a job shift. Exoskeletons have been developed to assist these types of motions, but require the user to bear the weight of a load through their body. Load carriage exoskeletons have been developed to offload worn mass from the user to the ground through the device structure, but they have had limited success and have not been well studied in manual materials handling tasks. In this paper, we introduce a hip-knee-ankle exoskeleton and two control methods: virtual model control and gravity compensation. We compared the ability of each controller to reduce lower-limb muscle activity during squatting. Because the virtual model controller is tailored to squatting, we hypothesized that it would outperform gravity compensation. Both controllers were able to reduce the activity of major lower-limb muscle groups during squatting when compared to squatting with the exoskeleton turned off. Contrary to our original hypothesis, the gravity compensation controller generally outperformed the virtual model controller, which may have been caused by the gravity compensation controller having more consistent knee torque application and the virtual model controller requiring better per-user tuning and familiarization. These results indicate the efficacy of both controllers in reducing injury risk in the lower limbs during squatting.
Exploration of the Generation of Water Metaphors in Chinese

According to the conceptual metaphor, this paper studies the characteristics and paths of water metaphor generation from the two aspects of the source domain and the target domain of word clustering. The results show that water, as a natural substance category, is the most common concept category in the source domain. Based on the attribute features of water, we combine the Word2Vec algorithm and word similarity computation to explore the path of generating water metaphor sentences, and finally realize the formation of “n+v” or “v+n” semantic conflict by examining the word similarity as a condition, so as to generate water metaphor sentences. At the same time, the language generation ability of the language model ChatGPT is tested, providing a reference for subsequent machine metaphor generation.
FISTA-NET: Deep Algorithm Unrolling for Cerenkov luminescence tomography

Cerenkov luminescence tomography (CLT) is a highly sensitive and promising imaging technique that can be used to reconstruct the three-dimensional distribution of radioactive probes in living animals. However, the accuracy of CLT reconstruction is limited by the simplified radiative transfer equation and ill-conditioned inverse problem. To address this issue, we propose a model-based deep learning network that combines the neural network with a model-based approach to enhance the performance of CLT reconstruction. The Fast Iterative Shrinkage Thresholding Algorithm (FISTA), a traditional model-based approach, is expanded into a deep network (known as FISTA-NET). Each layer in the network represents an iteration of the algorithm steps, and connecting these layers can form a deep neural network. In addition, different from the traditional FISTA, the key parameters in FISTA, such as gradient step size and threshold value, can be learned through training data without manual production. To evaluate the performance of FISTA-NET, numerical simulation experiments were conducted, which demonstrate its excellent positioning and shape recovery abilities.Clinical Relevance—This indicates that FISTA-NET strategy can significantly improve the quality of CLT reconstruction, which is further beneficial to the assessment of disease activity and treatment effect based on CLT.
A Light-Weight, Low-Cost, and Sustainable Planning System for UAVs Using a Local Map Origin Update Approach

This paper proposes a sustainable planning system for small-sized unmanned aerial vehicles (UAVs). Our mapping module of the system uses a voxel array as data structure with an introduced feature which is local map origin update. This approach has clear advantages that the planning system can sustainably plan trajectories regardless of operating radius and flight distance, and it shows fastest invariant time complexity $\mathcal{O}(1)$ unlike other representation methods. Also, we propose an efficient configuration space (C-space) construction algorithm using incremental voxel inflation, and extend state-of-the-art Euclidean signed distance field (ESDF) algorithm, FIESTA by applying the local map origin update feature. The proposed planning system requires single depth camera only as a sensor, and can operate in realtime on embedded computing platforms. We have verified the planning system through real-world flight tests in dense environments using a lightweight quadrotor plat-form under 300 mm size equipped with low-cost components only.
Using Compressive Sensing Technique in Image Sensing

This article presents a new structure of image sensor that compresses the image information based on the compressive sensing (CS) technique and employing a measurement matrix. In the proposed structure to perform compression, the image related voltages on the capacitors in each neighboring pixels are summed in a specific regular pattern. Also, the high sparsity of moving objects images which obtained from the difference images has been exploited and the proposed structure compresses the image information both in the normal and difference modes. Implementation of the measurement matrix and linear combination of the pixel data is done by simple connections between the pixels. The results of 300 images and 30 difference images compressed and recovered through a deterministic measurement matrix show the PSNR values of 26.65 dB and 40.09dB respectively. The results show the effect of applying presented method on the quality of recovered images.
Synthesis and Performance Analysis of UKF for Processing Measurements of UWB ToF/AoA LNS with Long Baseline and IMU

Year by year the question of solving tasks of high-precision indoor navigation, such as collision avoidance and tracking of industrial vehicles, personnel tracking, worker safety and geofencing services, location-based alerts etc. is becoming more and more acute. The most expedient way to solve these problems is the development of integrated systems based on UWB LNS and IMU. This choice is due to their complementary properties. The IMU measures with a high rate and low fluctuation component of error, at the same time they are characterized by non-stationary nature, and UWB LNS measures, in turn, with lower rate, higher fluctuation component of error, and stationary nature. In this paper algorithm of combined processing estimates of the PDR algorithm and measurements from ToF/AoA UWB LNS with a long baseline was synthesized. In conducted simulation experiments proposed algorithm for ToF/AoA UWB LNS with baseline length 4λ showed a gain of more than two times in DRMS of errors of estimates over the same metric for standard baseline (λ/2). In the carried out real experiment DRMS of errors of estimates of the proposed algorithm was 0.13 m.
Plug-and-Play Enhanced Compressive Sensing for Limited Sample PAT Image Reconstruction

Medical imaging technologies are crucial for precise disease diagnosis, encompassing X-rays, Magnetic Resonance Imaging (MRI), and Ultrasound scans. However, these methods have limitations such as ionizing radiation, cost, time consumption, or compromised image quality. Photoacoustic Tomography (PAT) is a promising noninvasive technique providing high-resolution, high-contrast images in deep tissues. In this paper we integrate the plug-and-play framework to enhance compressive sensing algorithms for PAT imaging with reduced samples as it offers faster image reconstruction, optimized memory, and expedited data transmission in real-world PAT applications. Our approach formulates and evaluates novel Split Bregman Total Variation (SBTV) and Relaxed Basis Pursuit ADMM (rBP-ADMM) algorithms fortified with Plug-and-Play (PnP) denoising techniques. These algorithms are assessed under diminishing sample conditions with various PnP denoisers. Notably, SBTV coupled with Bilateral Filtering as a PnP denoiser yields significant SSIM enhancements (11-17%). These findings highlight the potential of PnP-enhanced SBTV for robust image reconstruction in low-sample scenarios, addressing a critical need for efficient PAT applications.