Miracle-dz/UniFAR-FTUs huggingface dataset:
Slip Modeling and Estimation for a Planetary Exploration Rover: Experimental Results from Mt. Etna

For wheeled mobile systems, the wheel odometry is an important source of information about the current motion of the vehicle. It is used e.g. in the context of pose estimation and self-localization of planetary rovers, which is a crucial part of the success of planetary exploration missions. Depending on the wheel-soil interaction properties, wheel odometry measurements are subject to inherent errors such as wheel slippage. In this paper, a parameter-based approach for whole-body slip modeling and calibration is applied to a four-wheeled lightweight rover system. Details on the method for slip parameter calibration as well as the system-specific implementation are given. Experimental results from a test campaign on Mt. Etna are presented, showing significant improvements of the resulting wheel odometry measurements. The results are validated during a long range drive of approx. 900 m and discussed w. r. t. the advantages but also limitations of the method within a space exploration scenario.
Classification of Factors Influencing on Recuperation Braking of Urban Transport and Evaluation of Their Impact

The paper presents several approaches to the classification of factors influencing the efficiency of regenerative braking. The method of classification by the criterion of the subsystem in which the factor is manifested is considered. On the basis of simulation modeling, the influence of the mass of the vehicle, the coefficient of the main movement resistance, the type of control system and the auxiliary power was investigated. The change of the specific energy consumption on the bus of traction substations is considered as a criterion for the efficiency of regenerative braking. It is shown that a significant influence on the efficiency of energy recovery is provided by the type of control system of the vehicle and the auxiliary power.
Continuous Shared Control for Robotic Arm Reaching Driven by a Hybrid Gaze-Brain Machine Interface

The brain-machine interface (BMI) has been reported to offer the potential for controlling the assistive robot for the motor impaired people, using the non-invasively obtained electroencephalogram (EEG) signals. However, the EEG based BMI may not be sufficient and stable to drive the robot moving freely in its 2D or 3D workspace. The robot autonomy may provide assistance for the BMI users with the shared control paradigm. Nevertheless, users suffers from several limitations of the current shared control paradigms applied on BMI, e.g., loss of sense of control, high mental workload due to unintuitive control with the human-robot interface and fixed level of assistance. To overcome these drawbacks, we propose a new control paradigm for the robotic arm reaching task where the robot autonomy is dynamically blended with the gaze-BMI control from a user. In this paradigm, the hybrid gaze-BMI constitutes an intuitive and effective input to continuously control the robotic arm end-effector moving freely in its 2D workspace, with an adjustable speed proportional to the motion intention strength. Furthermore, the adjustable level of assistance by our paradigm allows the system to balance the user's capabilities and feelings of control while compensating for the reaching task's difficulty. The proposed paradigm is verified in the task where a healthy subject utilizes the hybrid gaze-BMI to control the robotic arm end-effector reaching for a target object while avoiding the obstacle in the path. The experimental results demonstrate that the movements with our shared control paradigm are safer, more efficient and less difficult than those without shared control.
A Modular Topology of Marx Generator Using Buck–Boost Converter

In this paper, a high-voltage unipolar pulse power generator is introduced for electroporation applications. Operating principle of the proposed structure is based on the buck–boost (BB) converter. The generator is comprised of parallel modules connected to the converter through an SCR and is supplied by a low-voltage dc source. Each module consists of an insulated gate bipolar transistor (IGBT), two diodes, and a capacitor. In fact, the proposed topology is a combination of the Marx generator and BB (MG-BB) converter. In comparison with other modular MG-BB structures, the proposed topology achieves interesting advantages including simpler structure, reduction of IGBTs voltage rating, and reducing number of components (inductors and switches). In order to clarify the features of the proposed structure, it has been compared with other unipolar MGs considering different aspects including the number of circuit components, number of required input supplies, amplitude of the output pulse, and the maximum voltage across switches and diodes. To validate the effectiveness of the structure, simulation and experimental results are presented. The results confirm that the proposed topology is capable of generating unipolar pulses with desirable amplitude and repetition frequency using a low-voltage dc supply.
Deadline-Based Scheduling for GPU with Preemption Support

Modern automotive-grade embedded computing platforms feature high-performance Graphics Processing Units (GPUs) to support the massively parallel processing power needed for next-generation autonomous driving applications (e.g., Deep Neural Network (DNN) inference, sensor fusion, path planning, etc). As these workload-intensive activities are pushed to higher criticality levels, there is a stronger need for more predictable scheduling algorithms that are able to guarantee predictability without overly sacrificing GPU utilization. Unfortunately, the real-rime literature on GPU scheduling mostly considered limited (or null) preemption capabilities, while previous efforts in broader domains were often based on programming models and APIs that were not designed to support the real-rime requirements of recurring workloads. In this paper, we present the design of a prototype real-time scheduler for GPU activities on an embedded System on a Chip (SoC) featuring a cutting edge GPU architecture by NVIDIA adopted in the autonomous driving domain. The scheduler runs as a software partition on top of the NVIDIA hypervisor, and it leverages latest generation architectural features, such as pixel-level preemption and threadlevel preemption. Such a design allowed us to implement and test a preemptive Earliest Deadline First (EDF) scheduler for GPU tasks providing bandwidth isolations by means of a Constant Bandwidth Server (CBS). Our work involved investigating alternative programming models for compute APIs, allowing us to characterize CPU-to-GPU command submission with more detailed scheduling information. A detailed experimental characterization is presented to show the significant schedulability improvement of recurring real-time GPU tasks.
A 1.7-mW −92-dBm Sensitivity Low-IF Receiver in 0.13- $\mu$ m CMOS for Bluetooth LE Applications

This paper presents a 1.7-mW low-intermediate-frequency receiver design for Bluetooth low-energy (BLE) applications. The design exploits particular aspects of BLE, such as the relaxed in-band interference characteristics, more precisely the <inline-formula> <tex-math notation="LaTeX">$C/I_{1~\textrm {MHz}}$ </tex-math></inline-formula>, and the relatively high-frequency-shift keying modulation index, to deliver a high level of energy efficiency and simplicity to the receiver baseband architecture. Reliable quadrature signals are generated in the RF signal path without consuming energy, which is supported by an inverter-based low-noise amplifier (LNA) that achieves high gain and low noise figure under low-power budgets. A small-signal analysis of low-power inverter-based LNAs is presented offering simple design equations. Seeking an affordable solution, the fabricated prototype is fully integrated into an earlier generation CMOS technology node (<inline-formula> <tex-math notation="LaTeX">$0.13~\mu \text{m}$ </tex-math></inline-formula>), occupying a silicon area smaller than 0.7 mm<sup>2</sup>. The receiver achieves a sensitivity level of −92 dBm while consuming 1.41 mA from a 1.2-V supply.
SC-Conv: Sparse-Complementary Convolution for Efficient Model Utilization on CNNs

We propose sparse-complementary convolution (SC-Conv) to improve model utilization of convolution neural networks (CNNs). The networks with SC-Conv achieve better accuracy than the regular convolution under similar computations and parameters. The proposed SC-Conv is paired with two deterministic sparse kernels, and one of kernels is complementary to the other one at in either spatial or channel domain or both; the deterministic sparsity increases the computational speed theoretically and practically; furthermore, by having the complementary characteristic, SC-Conv retains the same receptive field to the conventional convolution. This insightful but straightforward SC-Conv reuses of modern network architectures (ResNet and DenseNet), and at the same FLOPs and parameters, SC-Conv improves top-1 classification accuracy on ImageNet by 0.6 points for ResNet-101 and keep the same model complexity. Furthermore, SC-Conv also outperforms recent sparse networks by 1.3 points at top-1 accuracy for ImageNet, and after integrating SC-Conv with the sparse network, we further improve another 1.8 points accuracy at similar FLOPs and parameters.
Behavioral Modeling a Wireless Borehole Communication Channel for Increase of the Telemetry Transfer Accuracy

We describe a procedure of compact behavioral models design of the communication channel through a borehole pipe for measurement while drilling (MWD) microwave systems based on results of measurements of radio pulse signals during testing of the channel. Characterization of the microwave channel for the decline-directed borehole and a transmitter on the Gunn diode is described as an example of the offered process. The procedure is based on measurement of signal samples of the transmitter with the subsequent accounting of electrodynamical parameters, attenuation in the media, and on the pipe walls.
A New Characterization of Mobility for Distance-Bearing Formations of Unicycle Robots

In this paper, we present a new characterization of mobility for formations of unicycle robots defined by distance-bearing constraints. In fact, by introducing a simple reduction procedure which associates a prescribed formation with a “macro-robot”, we extend the classification by type proposed by Campion et al., to multi-agent systems. To simplify the classification task, which only leverages the nonslip condition for a conventional centered wheel, we assume that the robots are disposed at the vertices of a regular convex polygon. We demonstrate the practical utility of the notion of macro-robot in a trajectory-tracking control problem for a formation of unicycles.
Robust Fixed-Wing UAV Guidance with Circulating Artificial Vector Fields

This paper presents a guidance vector field strategy to control a fixed-wing UAV (unmanned aerial vehicle)subject to uncertainty in order to converge to and circulate a closed curve in $\mathbb{R}^{3}$. The control system is designed based on a reference model of the airplane with constrained input controls. The law is independent of the vector field's structure, however, some analysis considers a consolidated vector field approach. Asymptotic stability is proven with Lyapunov Theory and ultimate bounds are found when bounded uncertainties are taken into account. The control law is continuous except in the surroundings of the unavoidable field's singularities. A theorem ensures asymptotic convergence when a switch is made. Simulations with a 6 DOF, 12 states realistic aircraft model demonstrate the efficiency of the strategy and its advantages.
Online Human Muscle Force Estimation for Fatigue Management in Human-Robot Co-Manipulation

In this paper, we propose a novel method for selective management of muscle fatigue in human-robot co-manipulation. The proposed framework enables the detection of excessive fatigue levels of an individual muscle group while executing a certain task, and provides anticipatory robotic responses to distribute the effort among less-fatigued muscles of human arm. Our approach uses a machine learning technique to enable online predictions of muscle forces in different arm configurations and endpoint interaction forces. The estimated muscle forces are then used for the model-based estimation of muscle fatigue levels. Through optimisation, the fatigue management system can alter the task execution in a way that specific fatigued muscles are offloaded, while at the same time enables the production of task force using muscles with lower levels of fatigue. The main advantage of the proposed method is that it can operate online, and that all the measurements are performed by the robot sensory system, which can significantly increase the applicability in real-world scenarios. To validate the proposed method, we performed proof-of-concept experiments where the task of the human operator was to use a tool to polish an object that was manipulated by the robot.
Tracking-Based Depth Estimation of Metallic Pieces for Robotic Guidance

In order to perform safe robotic interventions in harsh environments it is necessary to help the robotic operator with a Human-Robot Interface that provides multimodal interactions, from low level interaction methods to bilateral teleoperation with force feedback. These interaction modalities, though, rely purely on the operator's skills. With the objective of providing a safer system, higher-level applications can be integrated in the interface in order to provide some help to the operator, without relying uniquely on his/her capacities. This paper presents a novel object recognition and tracking system which runs in real-time on the robot while the operator is operating it. The tracking system enters in the teleoperation loop and helps the operator to achieve the requested goals. The system is optimized to track featureless objects such as metallic plates, metallic connectors and monochromatic objects. Moreover, the algorithm provides improvements with respect to previous tracking experiments, including depth estimation in order to better interact with the velocity control of the robotic arm when approaching the target, as well as high reliability with partial occlusions. This vision-based control system is used in real interventions in hazardous environments, in order to track and manipulate metallic parts of scientific and engineering machines, giving a performance success over 95%, and reaching the 100% under the remote human supervision.
Securing Consumer IoT in the Smart Home: Architecture, Challenges, and Countermeasures

The consumer Internet of Things (IoT) platforms are gaining high popularity. However, due to the open nature of wireless communications, smart home platforms are facing many new challenges, especially in the aspect of security and privacy. In this article, we first introduce the architecture of current popular smart home platforms and elaborate the functions of each component. Then we discuss the security and privacy challenges arising from these platforms and review the state of the art of the proposed countermeasures. We give a comprehensive survey on several new attacks on the voice interface of smart home platforms, which aim to gain unauthorized access and execute over-privileged behaviors to compromise the user's privacy. To thwart these attacks, we propose a novel voice liveness detection system, which analyzes the wireless signals generated by IoT devices and the received voice samples to perform user authentication. We implement a real-world testbed on Samsung's SmartThings platform to evaluate the performance of the proposed system, and demonstrate its effectiveness.
Self-Supervised Learning of the Drivable Area for Autonomous Vehicles

We propose a new approach for generating training data for the task of drivable area segmentation with deep neural networks (DNN). The impressive progress of deep learning in recent years demonstrated a superior performance of DNNs over traditional machine learning and deterministic algorithms for various tasks. Nevertheless, the acquisition of large-scale datasets with associated ground truth labels still poses an expensive and labor-intensive problem. We contribute to the solution of this problem for the task of road segmentation by proposing an automatic labeling pipeline which leverages a deterministic stereo-based approach for ground plane detection to create large datasets suitable for training neural networks. Based on the popular Cityscapes [1] and KITTI dataset [2] and two off-the-shelf DNNs for semantic segmentation, we show that we can achieve good segmentation results on monocular images, which substantially exceed the performance of the algorithm employed for automatic labeling without the need of any manual annotation.
Learning and Generation of Actions from Teleoperation for Domestic Service Robots*This work was supported by JST, CREST

In this paper, we propose a method for motion learning aimed at the execution of autonomous household chores by service robots in real environments. For robots to act autonomously in a real environment, it is necessary to define the appropriate actions for the environment. However, it is difficult to define these actions manually. Therefore, body motions that are common to multiple actions are defined as motion primitives. Complex actions can then be learned by combining these motion primitives. For learning motion primitives, we propose a reference-point and object-dependent Gaussian process hidden semi-Markov model (RPOD-GP-HSMM). For verification, a robot is teleoperated to perform the actions included in several domestic household chores. The robot then learns the associated motion primitives from the robot's body information and object information.
The Installation Performance Control of Three Ducts Separate Exhaust Variable Cycle Engine

The research on modeling and installation performance control of three ducts separate exhaust variable cycle engine has been studied. In this paper, we established the fan, the low pressure and high pressure compressor model based on the stage-stacking method. On this basis, a component-level mathematical model is constructed and the compressor model of adjustable mode selector valve area, the third bypass and its exhaust nozzle mode are established. Besides, the research took the inlet inside and outside characteristic model into consideration. The simulation showed that the engine could be able to modulate the third bypass airflow adaptively by control of fan vane, mode selector valve area, third bypass, and main exhaust nozzle area. It is possible to continuously match the engine’s airflow demand to the inlet’s airflow supply. The results show that compared with the mixed exhaust three ducts variable cycle engine, the inlet spillage drag of the separate exhaust variable cycle engine is reduced by about 18%. Under the condition of supersonic flight, the constant flow control can significantly improve the economy of the engine. Compared with the mixed flow variable cycle engine, the inlet spillage drag of the three ducts separate exhaust variable cycle engine is reduced by about 20%.
Outage Probability Analysis of Power Splitting Power-Beacon Assisted Energy Harvesting Relay Wireless Communication Networks

In this paper, we derived the analytical expressions of the system performance (in term outage probability and throughput) of the power splitting half-duplex power beacon-assisted energy harvesting relay network in both amplify-and-forward and decode-and-forward modes. Moreover, the analytical results are also demonstrated and convinced by using Monte-Carlo simulation. The numerical results demonstrated and convinced the analytical and the simulation results are matched well with each other in connection with all possible system parameters.
Multiple Wind Power Time Series Modeling Method Considering Correlation

As growing penetration of wind power integrated into power system, effective model is demanded to capture the characteristics of wind power not only in statistics but in time dependency and spatial dependency. This paper proposes a novel model that integrating pattern recognition and Markov Chain Monte Carlo (MCMC) method. In order to embody the correlated variation of daily wind power at different sites, typical scenarios are obtained by historical multiple wind power data and clustering algorithm. A single-variable MCMC model is then established to describe the scenarios transition process. Next, a multi-variable MCMC models are established to describe the correlation existed in the daily time series of multiple wind farms. The typical scenario Markov chain and daily wind power sequences for each typical scenario state are simulated successively and then generated a complete multiple wind power sequences. The effectiveness test shows that the wind power time series generated by the proposed models show higher accuracy on the statistical characteristic, autocorrelation and crosscorrelation, compared with Copula model.
Mobile LoRa Gateway for Smart Livestock Monitoring System

Livestock is an essential commodity, especially in Indonesia, an agricultural country located in the Asia Pacific. Indonesia has vaster Greenland area than any other ASEAN countries. One way to increase livestock production is by implementing Smart Livestock Monitoring System which relies on Internet of Things technology such as LoRa. LoRa and other Low Power Wide Area (LPWA) technologies offer great advantages such as extended transmission range, extended node’s battery life, and a massive number of nodes per gateway support. However, LoRa utilizes sub 1 GHz unlicensed spectrum which has been very crowded since the last decades and will be even more crowded soon following IoT trends. We propose to use mobile LoRa gateway to mitigate this issue. This paper simulates and compares deployments of LoRa using one mobile gateway with one and multiple static gateways. LoRa is simulated using one mobile gateway and compared with one and multiple static gateway deployments. Simulation result shows that one static gateway is better for narrow livestock area because it gives sufficient Data Extraction Rate (DER) value required for data transmission and lowest Network Energy Consumption (NEC) value. Otherwise, for vast livestock area, one mobile gateway is better because of smaller deployment cost and more than sufficient DER value.
Performance Assessment of an Integrated Radar Architecture for Multi-Types Frontal Object Detection for Autonomous Vehicle

A hazard-free autonomous vehicle (AV) navigation requires the presence of a dependable perception module. For the frontal object and environment detection, the perception module needs to be able to provide a comprehensive detection region to allow blind spot monitoring. In this work, an integrated perception architecture for frontal object detection of an AV is introduced. The architecture is an integration of central, left and right sides radar for the frontal area. The radar placement of the design is expected to reduce the frontal blind spot region of the vehicle. The architecture’s performance is analyzed by assessing each of the radar’s performance. This includes their respective ability to measure multi-type of obstacles (pedestrian, black car and motorcycle) as well as evaluating the max range detection of the radars. The results show that each of the radar managed to detect multi-type obstacles as well as possessing a maximum detection range, which will allow for a reduced blind spot area and aids the hazard mitigation actions. The findings of this preliminary study are important to develop a more comprehensive perception module of an autonomous vehicle.