Miracle-dz/UniFAR-FTUs huggingface dataset:
Inverse Perspective Mapping-Based Neural Occupancy Grid Map for Visual Parking

Sensing environmental obstacles and establishing an occupancy map of surroundings are critical to achieving automated parking for autonomous vehicles. This paper presents a method to obtain surrounding occupancy information from inverse perspective mapping (IPM) images. This method uses the easily-accessed pseudo-labels from LiDAR to supervise a visual network, which can detect occupied boundaries of obstacles. Fusing this visual occupancy with ego-motion information, we develop a multi-frame fusion approach to build a local OGM to realize online environment mapping. Compared with other learning-based occupancy approaches, our method does not require time-consuming and labor-intensive labeling for the environment due to the ground truth of surrounding occupancy coming from LiDAR easily. The proposed method achieves LiDAR-like performance with pure visual inputs, which greatly decreases the cost of real products. Experiments on driving and parking environments prove that our method can accurately sense surrounding occupancy information and build a robust occupancy map of the environment.
Analysis of the Interconnection Capacity Calculation Methodologies for the European Electricity Market

The interconnected European transmission system for electricity supports the electricity trading across Europe, promoting the maximization of welfare, in particular when the interconnection capacity is enough to take low marginal cost generation, namely variable renewable power generation, into the demand areas where it is scarce. Transmission systems operators (TSOs) calculate the available interconnection capacity for the day-ahead and intraday markets in a coordinated way. This is a key step to enable price-based market coupling between different countries and regions and, therefore, efficient use of electricity from renewable energy sources and effective distribution of the benefits of renewable electricity across Europe. This work addresses the calculation of interconnection capacity between bidding zones by analyzing the processes and comparing the different methodologies used by European TSOs to coordinate the capacity calculation and compute the underlying generation shift keys (GSKs). The paper focus on the Portuguese cross-border transmission capacity with Spain, where the merit order methodology defines the GSKs supporting the calculation of the Available Transmission Capacity (ATC) for the spot market.
Noncontact Blood Pressure Estimation Using BP-Related Cardiovascular Knowledge: An Uncalibrated Method Based on Consumer-Level Camera

Objective: The tiny change of skin color, caused by a heartbeat, can be captured with consumer-level cameras by using the imaging photoplethysmography (iPPG) technique, offering a noncontact way of extracting pulse signals. Pulse signals have been demonstrated to contain information on human physiological characteristics and have been used for blood pressure (BP) estimation in recent years. According to BP-related cardiovascular knowledge, this article presents a new method for BP estimation based on the iPPG pulse signals, featured by incorporating cardiovascular characteristics including heart rate (HR), stroke volume (SV), the elasticity of vessel walls (EVW), and peripheral vascular resistance (PVR). Correlations between the systolic BP (SBP), diastolic BP (DBP), pulse pressure (PP), and cardiovascular characteristics are extracted, which facilitates the selection of pulse features consistent with BP properties. Based on the selected features, two Bayesian neural network (BNN) models are constructed for the estimation of SBP and DBP, respectively, where the machine learning (ML) uncertainty of the estimation is also evaluated. This method is uncalibrated which means it can work without additional information except for the videos from the camera. The proposed method has been tested on 220 patients with a history of cardiovascular diseases. Errors of the BP estimation are 9 ± 13 (MAE ± STD) mmHg for SBP, 7 ± 10 (MAE ± STD) mmHg for DBP, and the ML uncertainty of the estimation indicates the reliability of the proposed method.
An Analysis of Unified Manipulation with Robot Arms and Dexterous Hands via Optimization-based Motion Synthesis

Robot manipulation today generally focuses on motions exclusively with a robot arm or a dexterous hand, but usually not a combination of both. However, complex manipulation tasks can require coordinating arm and hand motions that leverage capabilities of both, much like the coordinated arm and hand motions carried out by humans to perform everyday tasks. In this work, we evaluate unified manipulation with robot arms and dexterous hands, using a motion optimization framework that synthesizes a series of configuration states over the entire manipulation system. We characterize the possible benefits of unifying arm and dexterous hand capabilities within a single model via metrics such as pose accuracy, manipulability, joint-space smoothness, distance to joint-limits, distance to collisions, and more. Several arm-hand combinations are quantitatively compared in simulation on a variety of experiment tasks and performance measures. Our results suggest that combining motions from robot arms and dexterous hands indeed has compelling benefits, highlighting the exciting potential of continued progress in unified arm-hand motion synthesis for robotics applications.
Comments on “An intelligent CMAC-PD torque controller with anti-over-learning scheme for electric load simulator”

This paper points out problems in a paper which appears in the Transactions of the Institute of Measurement and Control entitled “An intelligent CMAC-PD torque controller with anti-over-learning scheme for electric load simulator” by Bo Yang, Huatao Han and Ran Bao (Vol. 39, No. 2, pp.192–200, 2016). Their proposed neural-network weight update makes no intuitive sense: it introduces a term that keeps the output of the neural network close to its input. Here, a standard linear analysis shows that their proposed update applied to adaptive parameters will result in a large steady-state error in general; however for their machine a low steady state error results only because the ideal numerical value of the control signal in Volts happens to be close to the numerical value of the desired input signal in Newton-meters. Furthermore, the authors claim their weight update prevents overlearning, but do not conduct a Lyapunov analysis or even graph a measure of their weights in the results section. This paper shows that a standard Lyapunov analysis (which establishes uniformly ultimately bounded signals for traditional robust update modifications like leakage) fails to reveal a bound on signals for the proposed method. Moreover, simulations demonstrate weight growth that continues at a linear rate during a long simulation when using the proposed method.
Implementation and Optimization of Grasping Learning with Dual-modal Soft Gripper

Robust and efficient grasping of different objects is still an open problem due to the difficulty of integrating multidisciplinary knowledge such as gripper ontology design, perception, control, and learning. In recent years, learning-based methods have achieved excellent results in grasping various novel objects. However, current methods are usually limited to a single grasping mode or rely on different end effectors to grasp objects of different shapes. For human beings, our hands are capable of grasping various objects with changes in grasping methods and form of hands. In light of this, developing a gripper with similar performance could possibly improve the robot's gripping ability. In this paper, we design a dual-modal soft gripper (DSG) and propose a deep reinforcement learning (DRL) framework to implement the operations. Both of our grasping modes, namely enveloping and pinching, are achieved through the tendon drive system and the deformation of the spring steel plate, which enables the gripper to switch between the two grasping modes in real time. We also combined the cutting-edge achievements of deep learning and reinforcement learning to design an autonomous grasping algorithm based on Q-learning and a deep Q network. Moreover, to fully utilize the visual input from the sensor, we added semantic embeddings of target objects to facilitate the learning, which is especially useful in deciding the grasping method for objects previously unseen. We also evaluate our DRL framework in different scenarios, offering a detailed comparison of each grasping mode and the mixed method (with or without semantic information). Our design has proved efficient in reducing the number of failing grasping actions and improving the success rate when facing novel and tricky objects.
Pattern formation in time series systems due to viscoelastic behavior: Case studies in uniform distribution, normal distribution, stock market index, and music

A new methodology was introduced to investigate the pattern formation in time series systems due to their viscoelastic behavior. Four stochastic processes, uniform distribution, normal distribution, Nasdaq-100 stock market index, and a melody were studied within this context. The time series data were converted into vectorial forms in a scattering diagram. The sequential vectors can be split into its in-line (or conservative) and out-of-line (or dissipative) components. Thus, one can define the storage and loss modulus for conservative, and dissipative components, respectively. Instead of using the geometric Brownian equation which involves Wiener noise term, the changes were taken into consideration at every step by introducing “lethargy” concept and the deviation from it. Thus, the mathematics is somehow simplified, and the dynamical behavior of time series systems can be elucidated at every step of change. The viscoelastic behavior of time series systems reveals patterns of the viscoelastic parameters such as storage and loss modulus, and also of thermodynamic work-like and heat-like properties. Besides, there occur some minima and maxima in the distribution of the angles between the sequential vectors in the scattering diagram. The same is true for the change of entropy of the system.
Optical Nanowire Gratings: An Efficient Single Photon Collector and Refractometer

We propose an optical nanowire grating-based device for efficient single-photon coupling and refractive index sensing. The proposed structure enhances the coupling of photons into the fiber-guided modes using an inline fiber cavity mobilized with a single quantum emitter. The optimized structure has a low mode volume of 0.56 µm3 and a Q-factor of ~890. The system has a high Purcell factor of 20 and a coupling efficiency of~ 90% to the guided mode from a single quantum emitter. Also, the sensitivity of the sensor is calculated as 84.8 nm/RIU and a high figure of merit of 95.11 RIU−1 for a range of gaseous analytes. So, we believe that the proposed system can be used in quantum networks as well as in biosensor applications.
Experimental evaluation of a method for improving experiment design in robot identification

The control system of industrial robots is often model-based, and the quality of the model of high importance. Therefore, a fast and easy-to-use process for finding the model parameters from a combination of prior knowledge and measurement data is required. It has been shown that the experiment design can be improved in terms of short experiment times and an accurate parameter estimate if the robot configurations for the identification experiments are selected carefully. Estimates of the information matrix can be generated based on simulations for a number of candidate configurations, and an optimization problem can be solved for finding the optimal configurations. This work shows that the proposed method for improved experiment design works with a real manipulator, i.e. it is demonstrated that the experiment time is reduced significantly and the accuracy of the parameter estimate can be maintained or reduced if experiments are conducted only in the optimal manipulator configurations. It is also shown that the model improvement is relevant for realizing accurate control. Finally, the experimental data reveals that, in order to further improve the model accuracy, a more advanced model structure is needed for taking into account the commonly present nonlinear transmission stiffness of the robotic joints.
Effect of poling on piezoelectric performances of Nb-doped BZT-BCT prototypes

This work reports the ferroelectric-piezoelectric characteristic of Nb doped BZT-BCT sol gel thin films pristine and after an optimized poling process. Displacement spectrum of cantilever is reported for pristine and poled state. The two spectra comparison clearly outlines the effectiveness of poling treatment.
An LMI-Based Risk Assessment of Leader–Follower Multi-Agent System Under Stealthy Cyberattacks

This letter proposes a methodology for assessing the potential risk from cyberattacks in multi-agent systems (MASs). MASs inherently rely on communication between agents, rendering them more vulnerable to cyberattacks than single-agent systems. The impact of cyberattacks could lead to performance disruption and safety violations. To address these concerns, we propose a risk assessment method for MASs using reachability analysis which computes the reachable set of the MASs via a Lyapunov function and its corresponding linear matrix inequalities (LMIs). The proposed method can quantify the potential risk against cyberattacks at agent and entire system levels by deriving ellipsoidal over-approximated reachable sets. An illustrative example is provided to validate the potency of the method, which shows the risk associated with the formation control of a leader-following MAS in an adversarial environment with scattered obstacles.
ELITE: Edge Layer IoT Trustless Environment

Public Key Infrastructure and Certificate Authorities provide systems, domains, and devices with identities for authorization and authentication. More recently, Blockchain Technology has challenged the centralized legacy models and methods providing enhancements to data storage, traceability, and security. In addition, Self-Sovereign Identity, Decentralized Identifiers and Verifiable Credentials have transferred the ownership of identity and control of data, implementing local-first systems development and decentralization of data registries and verification. Secure key storage has encountered significant threats and vulnerabilities with extensive research and development into hardware modules, trusted execution environments and software-based solutions. However, the advantage of Physically Unclonable Functions without dependency on keystore in memory, removes the constant security challenges associated with traditional cryptographic key management. The combination of these technologies, protocols, standards, and development approaches provides an innovative and novel approach to identity within the Internet of Things. This paper provides a review of the current state-of-the-art whilst detailing the novel Edge Layer IoT Trustless Environment (ELITE) framework through the application of Physically Unclonable Function modules, Decentralized Identifiers, Verifiable Credentials, Layer 1 Blockchain Technology and Layer 2 scalability solutions.
Observability-Aware Active Extrinsic Calibration of Multiple Sensors

The extrinsic parameters play a crucial role in multi-sensor fusion, such as visual-inertial Simultaneous Localization and Mapping(SLAM), as they enable the accurate alignment and integration of measurements from different sensors. However, extrinsic calibration is challenging in scenarios, such as underwater, where in-view structures are scanty and visibility is limited, causing incorrect extrinsic calibration due to insufficient motion on all degrees of freedom. In this paper, we propose an entropy-based active extrinsic calibration algorithm leverages observability analysis and information entropy to enhance the accuracy and reliability of extrinsic calibration. It determines the system observability numerically by using singular value decomposition (SVD) of the Fisher Information Matrix (FIM). Furthermore, when the extrinsic parameter is not fully observable, our method actively searches for the next best motion to recover the system's observability via entropy-based optimization. Experimental results on synthetic data, in a simulation, and using an actual underwater vehicle verify that the proposed method is able to avoid the calibration failure while improving the calibration accuracy and reliability.
Supernumerary Robotic Limbs for Next Generation Space Suit Technology

This paper discusses the incorporation of a pair of Supernumerary Robotic Limbs (SuperLimbs) onto the next generation of NASA space suits. The wearable robots attached to the space suit assist an astronaut in performing Extra-Vehicular Activities (EVAs). The SuperLimbs grab handrails fixed to the outside of a space vehicle to securely hold the astronaut body. The astronaut can use both hands for performing an EVA task, rather than using one hand for securing the body or operating a tether. The SuperLimbs can also assist an astronaut in repositioning the body and stabilizing it during an EVA mission. A control algorithm based on Admittance Control is developed for a) virtually reducing the inertial load of the entire body so that an astronaut can reposition his/her body with reduced effort, and b) bracing the body stably despite reaction forces and disturbances acting on the astronaut during an EVA operation. A full-scale prototype of Space Suit SuperLimbs was constructed and tested. Results from the experimentation indicated that with the aid of SuperLimbs, energy consumption during EVAs is reduced significantly.
Investigating the Impact of Mechanical Deformation on Non-Invasive Serum Glucose Sensing Using a Double U-slot Antenna

Diabetes is a non-communicable, chronic disease for which the afflicted individual has impaired control over serum glucose levels. Presently, this disease requires patients to monitor their glucose levels to effect control, using invasive or minimally-invasive techniques. Presently non-invasive monitoring remains an elusive goal. In this paper a double u-slot patch antenna is investigated as the basis for non-invasive sensing. While the patch has been investigated for this purpose in the past, in this work, slot loading is proposed to improve the sensing ability, and the impact of mechanical deformation resulting from practical forces is explored using COMSOL Multiphysics. The impact of sensor deformation on observed reflection coefficient response between 1-6GHzacross different serum glucose levels was studied. Results suggest that while the reflection coefficient resonant frequency shift correlates with shifts in the serum glucose level, the u-slot loaded antenna was relatively insensitive to mechanical deformation, for the frequencies studied.
Round Trip Time Measurement Over Microgrid Power Network

A focus of the Power Systems and Networking communities is the design and deployment of Microgrid (MG) integration systems that ensure that quality of service targets are met for load sharing systems at different endpoints. This paper presents an integrated Microgrid testbed that allows Microgrids endpoints to share their current, voltage and power values using a Network Published Shared Variable (NPSV) approach. We present Round Trip Time (RTT) measurements for time sensitive Microgrid control traffic in the presence of varying background traffic as an example quality of service measurement. Numerical results are presented using a range of different background traffic traces. The results show that when the variance of background traffic is increased, this leads to an increase in the RTT value on a Layer-2 (L2) network connection.
Distributed Initialization for Visual-Inertial-Ranging Odometry with Position-Unknown UWB Network

In recent years, the visual-inertial-ranging (VIR) state estimator with a position-unknown UWB network has become popular. However, most existing VIR methods leverage centralized algorithms to initialize the UWB anchors, which are challenging to be applied to massive UWB networks. In this paper, we propose a distributed initialization method for consistent visual-inertial-ranging odometry with a position-unknown UWB network (DC-VIRO). For the position-unknown UWB anchors, we solve a Robot-aided Distributed Localization (RaDL) to initialize their positions. For robot state estimation, we fuse the ranging measurements of initialized anchors and visual-inertial measurements in a consistent filter. The RaDL is formulated as a consensus-based optimization problem and solved by the Distributed Alternating Direction Method of Multipliers (D-ADMM) algorithm. To identify the unobservable conditions, we propose a self-contained Fisher Information Matrix (FIM) based criterion which can be evaluated by each anchor directly with locally-preserved ranging measurements. We use Covariance Intersection (CI) to estimate the covariance of initialized anchors' positions for consistent data fusion. The proposed DC-VIRO is validated in both simulation and real-world experiments.
Experimental Demonstration of Hybrid LED-LD Based UOWC Link

In this paper, we have designed and experimentally characterized a hybrid light emitting diode (LED) and laser diode (LD) based underwater optical wireless communication (UOWC) link, which can be suitable for providing high-speed optical connectivity between onshore and submerged systems. An LED-based system is efficient for short-range communication whereas an LD-based system is for long distances. In this paper, we have experimentally demonstrated the establishment of a hybrid LED-LD UOWC link at a data rate of 10 Mbps over a 2.5 m water channel using a return to zero on-off keying (RZ-OOK) modulation scheme using an FPGA-based transmitter and receiver. We have also used offline data processing to quantify the bit error rate (BER) performance as a function of received optical power for tap water and turbid water channels. For the tap water channel, we have achieved a BER as low as 3.5 ×10−6 at - 35.1 dBm received optical power, whereas for turbid water the achieved BER is 1.8 × 10−5 at −31. 6 dBm of received optical power in absence of air bubble. In turbid water (and tap water) channel in presence of air bubbles the minimum achievable BER is around 3 × 10−4 (3 × 10−4) for −31.5 dBm (-35.5 dBm) of received power.
Safe Operations of an Aerial Swarm via a Cobot Human Swarm Interface

Command and control of an aerial swarm is a complex task. This task increases in difficulty when the flight volume is restricted and the swarm and operator inhabit the same workspace. This work presents a novel method for interacting with and controlling a swarm of quadrotors in a confined space. EMG-based gesture control is used to control the position, orientation, and density of the swarm. Inter-agent as well as agent-operator collisions are prevented through a velocity controller based on a distance-based potential function. State feedback is relayed to the operator via a vibrotactile haptic vest. This cobot human swarm interface prioritizes operator safety while reducing the cognitive load during control of a cobot swarm. This work demonstrates that an operator can safely and intuitively control a swarm of aerial robots in the same workspace.