Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Analysis of a 2-D Piezoelectric Platform Based on Three-Stage Amplification and L-Shaped Guiding

In piezoelectric micropositioning platforms, the displacement amplification mechanism can solve the small output displacement of the platform; the displacement coupling will also increase when the displacement is amplified. To solve the problem of multistage displacement amplification and displacement coupling after multistage displacement amplification, this study proposes a 2-D piezoelectric platform based on three-stage amplification and L-shaped guiding. First, the structure of the 2-D piezoelectric platform is designed and its working principle is described. Next, the 2-D piezoelectric platform is analyzed theoretically, and the static and dynamic characteristics of the structure are analyzed by the finiteelement method. Finally, a prototype of the 2-D piezoelectric platform is fabricated, and the theoretical and simulation results are analyzed experimentally. The experimental results reveal that the platform has a motion range of $31.8\times41\,\,\mu \text{m}$ . The multistage displacement amplification ratio is 21.8, and the displacement coupling error is less than 4.2%. The positioning error of the closed-loop control platform is less than 0.5%, and the trajectory tracking error is less than 1.6%. Compared with the traditional piezoelectric micropositioning platform, the three-stage amplification and L-shaped guiding mechanism can effectively improve the displacement magnification and decoupling performance of the piezoelectric micropositioning platform as well as the positioning ability and trajectory tracking ability of the platform under closed-loop control, which is expected to be utilized in fields such as multistage amplification micropositioning platforms and dynamic optical systems.
On the Dynamic Evolution of Distributed Computational Aggregates

Engineering and programming approaches for collective adaptive systems often leverage ensemble-like abstractions to characterise a subset of devices as a domain for a given task or computation. In this paper, we address the problem of programming the dynamic evolution of distributed computational aggregates, through neighbour-based coordination. This is a problem of interest, since several situated activities (especially in large-scale settings) require decentralised collaboration, and need to be sustained by limited subsets of devices. These subsets may vary dynamically due to delegation, completion of local contributions, exhaustion of resources, failure, or change in the device set induced by the openness of system boundaries. In order to study and develop how distributed aggregates progressively take form by local coordination, we build on the field-based framework of aggregate processes, and extend it with techniques to support more expressive evolution dynamics. We propose novel algorithms for more effective propagation and closure of the boundaries of dynamic aggregates, based on statistics on the information speed and a notion of progressive closure through wave-like propagation. We verify the proposed techniques by simulation of a paradigmatic case study of multi-hop message delivery in mobile settings, and show increased performance and success rate with respect to previous work.
Simulating and Optimizing Nasopharyngeal Swab Insertion Paths for use in Robotics

The nasopharyngeal swab is the standardized method of collecting specimens for diagnosing COVID-19, among numerous other respiratory illnesses. While there has been interest from the robotics community in the design of robots and manipulators for performing swab collections, detailed simulation and planning for swab insertion trajectories through the nasal cavity is less studied. In this work, we propose a simulation environment with the swab modelled as an Euler-Bernoulli beam, subject to linear elastic collisions coming from the nasal cavity. We evaluate the impact of inserting the swab with different amounts of force. We also leverage the simulation environment to pose an optimization problem that finds trajectories that minimize strain on the swab during the insertion. We find that the optimized trajectories adhere to qualitative clinical advice.
A Low-Cost 3D Printed Prosthetic Hand with a Sensory Feedback Interface

The usage of a prosthesis by children who lack an upper limb, is a promising way to improve the quality of life. However, rejection rate amongst users of prosthetic devices is high, besides other causes, due to high cost, high weight, lack of social acceptance and lack of sensory feedback. The growing availability and improvements of 3D printers offer a solution to some of these issues and many designs were created, but to the best of our knowledge without addressing the issue of sensory feedback. In this paper we present a low-cost, 3D printed, motorized prosthetic hand design with a sensory feedback interface based on a modality-matched approach. The design offers an end-to-end solution and was validated using an adapter for able-bodied users and an interface used to log the data. A relation between applied force and feedback signal was verified and was used to optimize and calibrate the sensors and feedback parameters. Moreover, analyzing the feedback when pressing a hard object compared to a soft object, yielded a significant difference in outcome. The entire design and interface are shared online freely and offer a platform for experiments on sensory feedback for prosthetic hands. The promising results show that the low-cost hand and sensory feedback interface performs as expected and based on previous research could potentially improve the functionality of the prosthesis and thereby improve the quality of life of children with an upper limb deficiency.
Uncertainty in Biometric Identification and Authentication Systems with Strong Secrecy

In this work, we study the uncertainty aspects in bio-metric identification and authentication systems. The uncertainty lies in two aspects, i.e., the probability mass function (p.m.f.) of users' biometric information and the observation channel. For the p.m. f. of users' biometrics, it is assumed that the users are from different groups and the distribution of each group is determined by the state. For the observation channel, it is assumed that there is a set of channels and the actual observation channel is from this set.
Numerical Simulation and Experiment for the Bottom Boundary of Riprap of Seawall by GPR

Seawall engineering is essential in preventing typhoon storm surge disasters in coastal areas. Usually, the engineering measure of throwing the stone to form a riprap layer is adopted to enhance seawall stability and anti-erosion property. Determining the thickness of the riprap layer is an essential step in the evaluation of engineering measurement, and the key is to determine the burial depth of the bottom boundary of the riprap. A seawall is taken as the research object. The propagation process of the electromagnetic wave in the seawall is simulated by the finite difference time domain (FDTD) method, the propagation law and profile response characteristics of the electromagnetic wave are obtained, and the method's feasibility is confirmed theoretically. Further field test and drilling detection results are used to calibrate the electromagnetic wave velocity, and the top and bottom interface of the riprap layer is divided, which provides a basis for the measurement of the riprap body.
TCP Davis: A Low Latency First Congestion Control Algorithm

The choice of feedback mechanism between delay and packet loss has long been a point of contention in TCP congestion control. This has partly been resolved, as it has become increasingly evident that delay based methods are needed to facilitate modern interactive web applications. However, what has not been resolved is what control should be used, with the two candidates being the congestion window and the pacing rate. BBR is a new delay based congestion control algorithm that uses a pacing rate as its primary control and the congestion window as a secondary control. We propose that a congestion window first algorithm might give more desirable performance characteristics in situations where latency must be minimized even at the expense of some loss in throughput. To evaluate this hypothesis we introduce a new congestion control algorithm called TCP Davis, which is a congestion window first algorithm that adopts BBR's approach of maximizing delivery rate while minimizing latency. In this paper, we discuss the key features of this algorithm, discuss the differences and similarity to BBR, and present results based on a real implementation.
Analysis of Charge Accumulation within Insulating Materials via Numerical Simulation

The high-voltage direct current (HVDC) electrical power transmission system utilizes direct current (DC) for bulk power transmission. Because of its numerous advantages which include system reliability and economic efficiency, HVDC transmission technology has been widely applied in the electrical engineering field. This paper presents a numerical simulation study to analyze charge accumulation profile within insulating materials which are Cross-Linked Polyethylene (XLPE), Low-Density Polyethylene (LDPE), and oil-impregnated paper insulation. This includes studying and explaining the effect of different values of permittivity and ambient temperature on the development of space charges within different types of insulating materials via numerical simulation. The bipolar charge transport (BCT) model is used to simulate the behavior of space charge in the insulation materials. The build-up of space charge within the insulation layer can affect the electric field distribution and, in extreme situations, it can lead to insulation failure. The fundamental findings of the numerical simulation in this paper reveal that changes in the ambient temperature and permittivity values could affect the dynamics and development of space charges within the insulating materials.
Online Scheduling of PV and Energy Storage System Based on Deep Reinforcement Learning

The Photovoltaic and Energy Storage System (PV-ES), as a typical microgrid, is increasingly become an important component of smart grid. Through effectively managing the energy operation of energy storage (ES), the PV-ES can effectively realize economic benefits, and provide power support for distribution networks (DN). However, the intermittency of PV power and the higher fluctuations of load power bring great uncertainty to the optimal scheduling of PV-ES. The optimality of traditional optimization methods for online scheduling can be insufficient because of the modeling complexity, strong conservatism, and heavy reliance on the prediction accuracy. To address these issues, this paper proposes an online ES scheduling algorithm for PV-ES based on deep reinforcement learning (DRL). Taking the system economic cost as the operation objective, DRL uses high dimensional PV-ES measurement data to directly drive the continuous action control of the energy storage, and can flexibly adapt to uncertain environments. Test results of a typical PV-ES show the superiority and effectiveness of this method.
Integrated Millimeter-Wave and Terahertz Analyzers for Biomedical Applications

This invited paper reviews the progress of silicon-based integrated analyzers for biomedical applications over the last two decades. Focus is set on various integrated circuit realizations in the millimeter-wave range from 30 GHz and at the terahertz frequencies of above 300 GHz. This article discusses high-frequency architectures and concrete implementations of both narrowband as well as broadband integrated readout circuits, including their use in miniaturized sensor solutions. A variety of circuits ranging from low-power sensing oscillators to highly integrated broadband vector network analyzers (VNAs), and example realizations of millimeter-wave cardiovascular sensors and implants to THz microfluidic labs-on-chip and exhaled human breath analyzers are recapitulated. This article closes with an outlook on emerging fields of research for future advancement of such biomedical sensors toward reliable and specific systems.
Electron-phonon scattering effects on electronic and optical properties of orthorhombic GeS

Group-VI monochalcogenides are attracting a great deal of attention due to their peculiar anisotropic properties. Very recently, it has been suggested that GeS could act as a promissory absorbing material with high input-output ratios, which are relevant features for designing prospective optoelectronic devices. In this work, we use the ab initio many-body perturbation theory to study the role of electron-phonon coupling on orthorhombic GeS. We identify the vibrational modes that efficiently couple with the electronic states responsible for giving rise to the first and second excitonic state. We also study finite-temperature optical absorption, and we show that even at $T\ensuremath{\rightarrow}0\phantom{\rule{4pt}{0ex}}\text{K}$, the role of the electron-phonon interaction is crucial to properly describe the position and width of the main experimental excitation peaks. Our results suggest that the electron-phonon coupling is essential to properly describe the optical properties of the monochalcogenides family.
Architecture proposal for real-time sensor monitoring using IoT and Augmented Reality

In this work, a greenhouse data acquisition system is proposed using the IoT protocol called MQTT, which is implemented in a Raspberry Pi configured as an MQTT broker. The augmented reality application is implemented in Unity for Android cell phones, when the cell phone camera detects the designed target, the application will create an informative 3D model of a greenhouse, in which the values of humidity in the soil, temperature, and ambient humidity, in addition to the level of carbon dioxide present in the air. These values add a dynamic feature that enhances the experience of a virtual laboratory or provides a distinctive detail to other projects that have augmented reality.
Mobile Integrated Access and Backhaul Node Backhaul Link Failure Recovery with Conditional Handover

Mobile integrated access and backhaul (IAB) has become an important topic and potential technology to discuss beyond the fifth generation (B5G) because the mobility of IAB nodes seriously affects the performance of networks and results in frequent backhaul radio link failure (BH RLF). In this paper, to recover from backhaul failures, we consider methods using the advanced preparation of alternative backhaul links and routes, such as conditional handover (CHO), to recover from backhaul failures. We propose two solutions to this issue, including cell selection based on parameter prediction and preconfigured priority and a force trigger CHO event in different scenarios. When RLF occurs in the mobile IAB nodes own BH links, the priority of the CHO candidate cell should be increased, effectively improving the selection probability of CHO candidate cells. When RLF occurs in the mobile IAB nodes' parent BH link, we propose a new CHO trigger event to ensure that when its own link is intact, CHO will be triggered. Through theoretical analysis, we show that our solutions effectively reduce backhaul link failure.
Lower-Limb Exoskeleton for Load-Carrying Augmentation

Exoskeleton robots as a kind of wearable robots can assist or enhance people’s locomotion ability. In this paper, we present a lower-limb exoskeleton robot for load-carrying augmentation. The exoskeleton had 6 degrees of freedom (DOF) per leg, and two of DOF were powered by motors. Two geared motors were integrated at the hip joint to drive hip and knee joints by cables. The exoskeleton was anthropomorphically designed for better user experience. The STM32 hardware platform and the controller area network (CAN) bus were applied for signal processing and transmission, respectively. The control algorithm applied proportional derivative (PD) control algorithm based on gravity and friction compensations to achieve human-machine cooperation. At last, the exoskeleton prototype with 20kg payload capacity was successfully developed. The assistance efficiency was tested by the heart rate method. And the results showed that the assistance efficiency reached up to 79.45%.
FBW-SNN: A Fully Binarized Weights-Spiking Neural Networks for Edge-AI Applications

This study proposes a Fully Binarized Weight Spiking Neuron Network (FBW-SNN). The core network is built based on XNOR arrays with binary weights where the weights of the first and the last layers are represented by a stochastic binary stream (stochastic numbers). We also introduced an algorithm that combines straight-through and surrogate gradients to train FBW-SNN. The evaluation results on the CIFAR-10 dataset show that the proposed FBW-SNN could achieve an accuracy of 82.68% with only 14-time steps, which is comparable to the accuracy of the Binarized SNN (with real weights at the first and the last layer) and conventional SNNs. By fully binarized, the proposed network model could be a promising candidate for Edge-AI applications implemented on low-power and resource-constrained devices.
Compare and Analyze the Transmit Interference Temperature Limit Algorithm

For decades, more and more research has been done on the development of wireless network communication, namely from 1G to 5G, and even the 6G network communication that currently being studied. The problems in the development of wireless network are being solved one by one, but there are still new challenges in our research process. For example, the computing system for each user is selfishly compete which leads to highly inefficiency and the tragedy of the commons. Under this circumstance, this paper intends to study how to optimize the frequency allocation of today's wireless communication, based on several popular frequency allocation algorithms, using MATLAB to model and analyze the experiments of Transmit Interference Temperature Limit (tx-ITL) with fixed S, Self-Optimizing tx-ITL, FLG (forward-looking Game) and simultaneous water-filling (SWF), four algorithms. After that, they are analyzed and compared their performance under the same conditions. This paper can help people to have a rough study and understanding of these methods, so as to make inspiration for the next stage or research for better performance of new methods.
CONSTRUCTION AND VALIDATION OF A NOVEL THREE-DIMENSIONAL TRUNK MUSCULOSKELETAL MODEL

Studies of internal biological forces during motion using musculoskeletal models have mainly focused on the extremities. Few reports have examined internal biological forces in spine motion using a trunk model. The aim of this study was to analyze detailed three-dimensional motion of healthy adults using a novel trunk model, and estimate internal biological forces in a standing position. We constructed a three-dimensional trunk musculoskeletal model. Dimensions of the vertebrae, other segments such as upper or lower extremities and muscles were based on a 31-year-old healthy man. Joint angle data for trunk and extremities kinematics were obtained from a standing position using a three-dimensional motion analysis system. To analyze motion of the spine in detail, we applied markers to three different places in each vertebral body from C7 to L5. Flexion moments accorded with spinal curvature at the apex of curvature of the thoracic spine at T8-9. Mean intradiscal pressure calculated from muscle strength was 802.9 N. The thoracolumbar three-dimensional trunk musculoskeletal model generated in the present study could potentially be used to analyze spinal moment and trunk muscle strength during static and dynamic motions.
H0LiCOW – IX. Cosmographic analysis of the doubly imaged quasar SDSS 1206+4332 and a new measurement of the Hubble constant

We present a blind time-delay strong lensing (TDSL) cosmographic analysis of the doubly imaged quasar SDSS 1206+4332 . We combine the relative time delay between the quasar images, Hubble Space Telescope imaging, the Keck stellar velocity dispersion of the lensing galaxy, and wide-field photometric and spectroscopic data of the field to constrain two angular diameter distance relations. The combined analysis is performed by forward modelling the individual data sets through a Bayesian hierarchical framework, and it is kept blind until the very end to prevent experimenter bias. After unblinding, the inferred distances imply a Hubble constant H_0 = 68.8|$^{+5.4}_{-5.1}$| km s^−1 Mpc^−1, assuming a flat Λ cold dark matter cosmology with uniform prior on Ω_m in [0.05, 0.5]. The precision of our cosmographic measurement with the doubly imaged quasar SDSS 1206+4332 is comparable with those of quadruply imaged quasars and opens the path to perform on selected doubles the same analysis as anticipated for quads. Our analysis is based on a completely independent lensing code than our previous three H0LiCOW systems and the new measurement is fully consistent with those. We provide the analysis scripts paired with the publicly available software to facilitate independent analysis (footnote with link to www.h0licow.org). The consistency between blind measurements with independent codes provides an important sanity check on lens modelling systematics. By combining the likelihoods of the four systems under the same prior, we obtain H_0 = 72.5|$^{+2.1}_{-2.3}$| km s^−1 Mpc^−1. This measurement is independent of the distance ladder and other cosmological probes.
Millimeter Wave Wideband Filtering Balun Based on LCP Self-Packaged Suspended Coplanar Waveguide Microstrip Hybrid Structure

In this report, a millimeter wave wideband filtering balun based on LCP multi-layer suspended coplanar waveguide (CPW) and microstrip hybrid structure is proposed, which is composed of four two-stage folded SIRs. The differential filtering path is constructed by using the inherent common mode suppression of symmetrical resonators and the electromagnetic coupling among resonators, realizing the integrated design of balanced-unbalanced signal conversion and filtering function. This filtering balun is designed centering at 32 GHz, with a 15dB bandwidth of 17.9 GHz (55.9%), an excellent phase imbalance in the passband of less than 4.9° and an amplitude imbalance of less than 0.68 dB. The whole circuit also has the advantages of simple structure, small size and self-packaged.
X-Parameter Modeling for Microwave Power Device Based on GRNN

The nonlinear parameter extraction for microwave power devices has the problems of low efficiency and high complexity. An X-parameter modeling based on general regression neural network (GRNN) is proposed in this paper. It is demonstrated that the GRNN model can realize the fit rate of 96.0 % and the mean-square error (MSE) of 0.0013. Consequently, the nonlinear characteristics for transistor can be accurately characterized by GRNN model.