Miracle-dz/UniFAR-FTUs huggingface dataset:
An Origami-Based Miniature Jumping Robot with Adjustable Jumping Trajectory and Enhanced Intermittent Jumps

A small-scale jumping robot can reach obstacles much larger than its size. It is important for a jumping robot to perform intermittent jumps to cross through rough terrains. However, the limitations of conventional structures hinder the further integration of functions to a miniature (sub-50 g) jumping robot. No sub-50 g jumpers could perform intermittent jumps with adjustable jumping trajectories. In this work, we proposed an origami-based miniature jumper, which performed intermittent jumps with adjustable omni-directional trajectories. The intermittent jumps were achieved by the jumping and self-righting mechanisms, which were actuated by a single motor. The clockwise and counterclockwise rotation of the motor actuated the loading, self-righting and triggering process, respectively. The jumping height was adjustable by adjusting the rotation angle of the motor. Meanwhile, the take-off pitch & yaw angle adjustment methods were integrated into the robot. Therefore, we demonstrated a 9 cm, 13.5 g prototype with functions of re-loading, self-righting, jumping height adjustment and take-off pitch & yaw angle adjustment. The robot could adjust jumping height from 16 to 34 cm and self-right for the next jump. The results revealed that our robot could jump across different obstacles with different scales and directions. The mobility was greatly increased compared with other miniature jumping robots.
Developmental conditions and culture medium influence the neuromodulated response of in vitro cortical networks

Goal of this work is to show how the developmental conditions of in vitro neuronal networks influence the effect of drug delivery. The proposed experimental neuronal model consists of dissociated cortical neurons plated to Micro-Electrode Arrays (MEAs) and grown according to different conditions (i.e., by varying both the adopted culture medium and the number of days needed to let the network grow before performing the chemical modulation). We delivered rising amount of bicuculline (BIC), a competitive antagonist of GABAA receptors, and we computed the firing rate dose-response curve for each culture. We found that networks matured in BrainPhys for 18 days in vitro exhibited a decreasing firing trend as a function of the BIC concentration, quantified by an average IC50 (i.e., half maximal inhibitory concentration) of 4.64 ± 4.02 µM. On the other hand, both cultures grown in the same medium for 11 days, and ones matured in Neurobasal for 18 days displayed an increasing firing rate when rising amounts of BIC were delivered, characterized by average EC50 values (i.e., half maximal excitatory concentration) of 0.24 ± 0.05 µM and 0.59 ± 0.46 µM, respectively.Clinical Relevance— This research proves the relevance of the experimental factors that can influence the network development as key variables when developing a neuronal model to conduct drug delivery in vitro, simulating the in vivo environment. Our findings suggest that not considering the consequences of the chosen growing conditions when performing in vitro pharmacological studies could lead to incomplete predictions of the chemically induced alterations.
Learning Robotic Powder Weighing from Simulation for Laboratory Automation

This study focuses on a robotic powder weighing task used in laboratory automation. In this task, a robot weighs a certain amount of powder with a milligram-level target mass using a dispensing spoon. The complex dynamics of the powder, the variations in the materials being weighed, and the need to balance conservative and aggressive actions are significant challenges in the robotics field. Therefore, learning approaches are critical for this task. However, many learning interactions in real-world environments require substantial efforts to clean the spread powder. To overcome this issue, this study employs a sim-to-real transfer learning approach using a domain randomization (DR) technique. This enables the robot to weigh various powders with a small target mass and alleviates the burden of collecting data in a real-world environment. Herein, we formulated weighing manipulation as a reinforcement learning problem. Besides, we developed a powder weighing simulator and carefully selected the dynamics parameters used for DR to adapt to unseen environments. A recurrent neural network-based policy was adopted considering the balance of conservative and aggressive actions. The sim-to-real zero-shot transfer experiments demonstrated that the robot completed the weighing tasks with an average weighing error of 0.1 - 0.2 mg for different powder materials and target masses (5 - 15 mg). Overall, this approach shows promising results and can be useful for automating laboratory tasks that involve weighing powders.
DynGMP: Graph Neural Network-Based Motion Planning in Unpredictable Dynamic Environments

Neural networks have already demonstrated attractive performance for solving motion planning problems, especially in static and predictable environments. However, efficient neural planners that can adapt to unpredictable dynamic environments, a highly demanded scenario in many practical applications, are still under-explored. To fill this research gap and enrich the existing motion planning approaches, in this pa-per, we propose DynGMP, a graph neural network (GNN)-based planner that provides high-performance planning solutions in unpredictable dynamic environments. By fully leveraging the prior exploration experience and minimizing the replanning cost incurred by environmental change, DynGMP achieves high planning performance and efficiency simultaneously. Empirical evaluations across different environments show that DynGMP can achieve close to 100% success rate with fast planning speed and short path cost. Compared with existing non-learning and learning-based counterparts, DynGMP shows very significant planning performance improvement, e.g., at least 2.7×, 2.2×, $2.4\times$ and $2\times$ faster planning speed with low path distance in four environments, respectively.
DeepPuff: Utilizing Deep Learning for Smoking Behavior Identification in Free-living Environment

A comprehensive assessment of cigarette smoking behavior and its effect on health requires a detailed examination of smoke exposure. We propose a CNN-LSTM-based deep learning architecture named DeepPuff to quantify Respiratory Smoke Exposure Metrics (RSEM). Smoke inhalations were detected from the breathing and hand gesture sensors of the Personal Automatic Cigarette Tracker v2 (PACT 2.0). The DeepPuff model for smoke inhalation detection was developed using data collected from 190 cigarette smoking events from 38 medium to heavy smokers and optimized for precision (avoidance of false positives). An independent dataset of 459 smoking events from 45 participants (90 smoking events in the lab and 369 smoking events in free-living conditions) was used for testing the model. The proposed model achieved a precision of 82.39% on the training and 93.80% on the testing dataset (95.88% in the lab and 93.78% in free-living). RSEM metrics were then computed from the breathing signal of each detected smoke inhalation. Results from the RSEM algorithm were compared with respiratory metrics obtained from video annotation. Smoke exposure metrics of puff duration, inhale-exhale duration, and inhalation duration were not statistically different from the ground truth generated through video annotation. The results suggest that DeepPuff may be used as a reliable means to measure respiratory smoke exposure metrics collected under free-living conditions.
ECG Classification with Dual Models: XGBoost Voting and Deep Learning with Attention

During the process of Electrocardiogram (ECG) recognition, frequent challenges arise concerning the lack of convenience and accuracy. Moreover, the imbalanced distribution of ECG data presents significant hurdles for ECG recognition models. To address the aforementioned issues, the paper proposes two ECG recognition models on a dataset enhanced through Generative Adversarial Network (GAN): XGVoting and CRANet, which are specifically designed to tackle the challenges of lightweight design and accuracy improvement. By utilizing GAN for data augmentation, the ECG dataset is effectively balanced. The first model, XGVoting, is a lightweight approach that intelligently combines the ensemble tool eXtreme Gradient Boosting (XGBoost) with voting, thereby enabling swift and efficient detection. The second model, CRANet, is meticulously designed to achieve high-accuracy recognition by effectively integrating Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and self-attention mechanisms. The efficacy of the proposed models is validated using the Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia Database (MIT-BIH). Remarkably, under the premise of lightweight design, XGVoting attains an impressive overall accuracy of 97.37%, while ensuring a perfect 100% accuracy in identifying normal heart-beats. Meanwhile, CRANet exhibits outstanding performance with an overall accuracy of 99.0%.
Security-Aware Ad Hoc Networking Protocol for Vehicular Safety Systems

Modern cars are implemented with a number of environmentally-aware safety systems, from radar-induced braking, to oncoming-vehicle detecting headlight controls. Similarly, with the dawn of semi and fully-autonomous vehicles, cars themselves are being equipped with an even wider range of sensors and are able to respond to more stimuli than ever. In this world, inter-vehicular information sharing and vehicle-to-infrastructure communication to exchange road-safety related message is of paramount importance. At the same time, message exchange should conform to standard security goals of authentication, integrity and anonymity. Our proposed system uses a bandwidth-efficient communication protocol which prioritizes message retransmissions by other participating vehicles in a manner that results in low latency and propagation over a wider area on the roadway. Communication between vehicles and RSUs is being protected by pseudo IDs and Hadamard matrix credentials for each vehicle and RSU.
Development of a Compact Linearly Polarized Wideband Hybrid Antenna Using Semi-Elliptical SIW Resonator and Patch Radiator

This paper introduces a novel compact linearly polarized (LP) wideband hybrid resonator antenna (HRA) using substrate integrated waveguide (SIW) technique based on a semi-elliptical resonator and patch radiator. The cavity is tuned at its fundamental resonant frequency, which excites the patch radiator, so the resonant frequencies of the two resonators are merged to support broadband operation. The proposed HRA is numerically investigated by HFSS software, and the simulated outcomes, including $S$11, impedance bandwidth, co-, and cross-polarized patterns, Half Power Beamwidth (HPBW), and variation of gain versus frequency, are reported. The simulated results show that the proposed hybrid resonator antenna offers a fractional impedance bandwidth (FBW) of 10.9% with a minimum gain of 6.55 dB.
Soft Cap for Vine Robots

Growing robots based on the eversion principle are known for their ability to extend rapidly, from within, along their longitudinal axis, and, in doing so, reach deep into hitherto inaccessible, remote spaces. Despite many advantages, vine robots also present significant challenges, one of which is maintaining sensory payload at the tip without restricting the eversion process. A variety of tip mechanisms have been proposed by the robotics community, among them rounded caps of relatively complex construction that are not always compatible with functional hardware, such as sensors or navigation pouches, integrated with the main eversion structure. Moreover, many tip designs incorporate rigid materials, reducing the robot's flexibility and consequent ability to navigate through narrow openings. Here, we address these shortcomings and propose a design to overcome them: a soft, entirely fabric based, cylindrical cap that can easily be slipped onto the tip of vine robots. Having created a series of caps of different sizes and materials, an experimental study was conducted to evaluate our new design in terms of four key aspects: vine robot made from multiple layers of everting material, solid objects protruding from the vine robot, squeezability, and navigability. In all scenarios, we can show that our soft, flexible cap is robust in its ability to maintain its position and is capable of transporting payloads such as a camera across long distances. We also demonstrate that the robot's ability to move through restricted aperture openings and indeed its overall flexibility is virtually unhindered by the addition of our cap. The paper discusses the advantages of this design and gives further recommendations in relation to aspects of its engineering.
Training Induced Improvement on Human Perception of the Force Feedback Provided by A Soft Robotic Glove

Creating haptic interface by glove-based wearable robotic system has become an increasingly interested topic in the area of human robotic interaction. Many force feedback gloves are constructed based on soft actuators. However, the recent development of haptic and force feedback technology mainly focused on the advancement of the actuating components and mechanism, and innovation of the force feedback rendering algorithms. It seems that another important part of this human-robot-interaction loop, i.e. the human factors, were understudied. Here, this study focused on the learning effect in haptic perception. We designed a pneumatic muscle-based force feedback robotic glove which can provide customized force feedback to the dorsal surface of each finger. An experiment was carried out with a specifically training procedure and evaluation on the force feedback perceptions. The results show that practice-induced improvement can be achieved by this training, allowing people have a better perception of the force feedback provided by this glove.
Fast Codeword Design for Asymmetric Millimeter-Wave MIMO Systems under Mutual Coupling

Asymmetric mmWave massive multiple-input multiple-output (MIMO) systems have attracted much interest due to the massive available spectrum and lower power consumption. In these systems, once a communication link has been established in the beam training stage, part of the array can be deactivated to support asymmetric uplink-downlink (UL-DL) traffic in the data transmission stage. For this array with deactivated elements, however, the phase of the original ideal codeword requires further perturbation to overcome the pattern distortion caused by mutual coupling between the activated and deactivated elements. We therefore propose a phase perturbation method, which perturbs the phase of ideal codeword based on the effective channel including the coupling effect. In this method, we propose a fast search (FS)-based alternative optimization algorithm that alternatively adjusts each activated antenna phase to apply to the effective channel. Simulation results show that the proposed phase perturbation method can make up for the angular distortion.
Capacity of the Mixed Gaussian-Impulsive Noise Channel

Communication systems suffer from the mixed noise which contains both non-Gaussian impulsive noise (IN) and white Gaussian noise (WGN) in many scenarios. However, there is little literature on the channel capacity under the mixed noise. In this paper, we analyze the existence of the capacity under p-th moment constraint and prove that there are only finite mass points in capacity-achieving distribution. Furthermore, we discuss the capacity relating to specific modulation format under the mixed noise and derive a lower bound with closed form. Numerical results reveal that the capacity of the mixed noise channel will decrease as the impulsiveness of the mixed noise becomes significant and the obtained analytical capacity lower bound is tight.
Multi-Scale Point Octree Encoding Network for Point Cloud Based Place Recognition

Over the past decades, point cloud-based place recognition has garnered significant attention. This research paper presents a pioneering approach, denoted as the Multi-scale Point Octree Encoding Network (MPOE-Net), designed to acquire a discriminative global descriptor for efficient retrieval of places. The key element of the MPOE-Net is the point octree encoding module, which adeptly captures local information for each point by considering its nearest and farthest neighbors. Further enhancing local relationships, a multi-transformer network is introduced, utilizing a novel grouped offset-attention mechanism. To amalgamate the multi-scale attention maps into a comprehensive global descriptor, a multi-NetVLAD layer is incorporated. Through rigorous experimentation across diverse benchmark datasets, our proposed method unequivocally outperforms existing techniques in the realm of point cloud-based place recognition tasks, achieving state-of-the-art results. Our code is released publicly at https://github.com/Zhilong-Tang/MPOE-Net.
Printable Bistable Structures for Programmable Frictional Skins of Soft-Bodied Robots

Soft robots made of flexible materials are highly adaptive, easy to fabricate, and safer to interact with. One of the ways for soft robots to interact with the surrounding environment is through their deformable bodily characteristics including internal body stiffness and external body friction. Though the flexibility of soft-bodied robots has been rigorously studied, the frictional skin of such soft-bodied robots, acting as a mechanical interface between the robot and the environment, remains unexplored. Being able to design the frictional skin will make soft-bodied robots more versatile in environmental navigation, more dexterous in manipulation tasks, and more flexible in haptic feedback. In this paper, we propose a robotic skin that can be programmed dynamically to change the mode of friction. The robotic skin is based on bistable bellow structures that can be switched between two folding states to change the contact points between the robotic skin and the ground. Our robotic skin can dynamically change its anisotropic frictional behavior to add another dimension to the designing space of soft robotics.
3D-Engineered Muscle Tissue as a Wireless Sensor: AntennAlive [Bioelectromagnetics]

Synthetic biology has the potential to drastically change sensor technologies. Living cells can now be genetically programmed to be responsive to specific molecules. If those living cells are supported to live inside a host body, in vivo sensing at a molecular level can be achieved. However, the communication link between the genetically modified cells and the outside of the host body is an open area of research. Here, we propose a generic wireless communication platform that we call AntennAlive to bridge this gap. The link described can be considered as a gateway between the molecular links that are used by living cells and electromagnetic links. It is a groundbreaking proposition that will initiate a new area of research where antenna design meets synthetic biology. As an example, a biohybrid implant consisting of engineered skeletal muscle and a reconfigurable implant antenna is described. The implant antenna is used as a passive reflector that is reconfigured by the contraction of the skeletal muscle initiated with a trigger molecule that we are interested in sensing. This reconfiguration is tracked by an on-body reader that completes the wireless link. Note that here sensing is realized with skeletal muscle for the first time in the literature.
SKDCPM algorithm can improve the single-trial decoding performance of very similar error-related potentials *

Error related potential (ErrP) is an effective control signal for the brain-computer interface (BCI). Current ErrP decoding methods can only distinguish right and wrong mental states. However, in real scenarios, error conditions often contain more detailed information, such as the degree of error, which would induce very similar ErrPs. Distinguishing such ErrPs effectively is of vital importance to provide more detailed information for optimizing BCIs. Hereto, a major challenge is the EEG differences of very similar ErrPs are very small. Thus, it is necessary to develop new efficient method for decoding very similar ErrPs. This study newly proposed an algorithm named shrinkage discriminant canonical pattern matching (SKDCPM), and compared its decoding results with the linear discriminant analysis (LDA), shrinkage LDA (SKLDA), stepwise LDA (SWLDA), Bayesian LDA (BLDA) and the DCPM, which were algorithms commonly used for ErrP decoding. A data set of 18 subjects was built, it had four conditions, i.e., right (0°), errors with varying degrees, i.e., 45°, 90°, 180° deviation from the predicted direction. As a result, the SKDCPM had high balanced accuracy (BACC) in right-wrong classification (0° vs. others). More importantly, it achieved a grand averaged BACC of 69.54% with the highest up to 74.25%, which outperformed all the other algorithms in very similar ErrPs decoding (45° vs. 90° vs. 180°) significantly. This study could provide new decoding methods for developing the ErrP-based BCI system.
Data-Driven Modeling and Experimental Validation of Autonomous Vehicles Using Koopman Operator: Distribution A: Approved for Public Release; Distribution Unlimited. OPSEC # 7248

This paper presents a data-driven framework to discover underlying dynamics on a scaled F1TENTH vehicle using the Koopman operator linear predictor. Traditionally, a range of white, gray, or black-box models are used to develop controllers for vehicle path tracking. However, these models are constrained to either linearized operational domains, unable to handle significant variability or lose explainability through end-2-end operational settings. The Koopman Extended Dynamic Mode Decomposition (EDMD) linear predictor seeks to utilize data-driven model learning whilst providing benefits like explainability, model analysis and the ability to utilize linear model-based control techniques. Consider a trajectory-tracking problem for our scaled vehicle platform. We collect pose measurements of our F1TENTH car undergoing standard vehicle dynamics benchmark maneuvers with an OptiTrack indoor localization system. Utilizing these uniformly spaced temporal snapshots of the states and control inputs, a data-driven Koopman EDMD model is identified. This model serves as a linear predictor for state propagation, upon which an MPC feedback law is designed to enable trajectory tracking. The prediction and control capabilities of our framework are highlighted through real-time deployment on our scaled vehicle.
Spin Wave Driven Skyrmionium based Transistor with PMA Barrier

Spin wave is one of the most prominent driving mechanisms for designing nanoscale spintronic devices. It generates less heat than electric current due to the fact that there is no movement of charged particles. Hence, spin-wave driven devices are immune to Joule heating that is often present in traditional electronic devices. In this paper, it is demonstrated that spin wave driven skyrmionium is able to achieve transistor functionality by employing a perpendicular magnetic anisotropy (PMA) barrier on a nanotrack. This PMA barrier creates different energy state within the barrier region that is responsible for pinning of the skyrmionium. Moreover, the velocity of the skyrmionium is approximately 30% higher than the skyrmion. This is due to the fact that skyrmionium have net zero topological charge so there is no transversal deflection. In order to facilitate the proper functioning, it is necessary to consider the amplitude and frequency of the excitation field within a specific range. The proposed device, that functions as a transistor by employing microwave fields with frequencies of 80 GHz and amplitudes of 0.6T without modifying the edges of the nanotrack, has the potential for developing future non-charge-based spintronic circuits.