Miracle-dz/UniFAR-FTUs huggingface dataset:
Fixed-root Aerial Manipulator: Design, Modeling, and Control of Multilink Aerial Arm to Adhere Foot Module to Ceilings using Rotor Thrust

Precise aerial manipulation is important for multirotor robots. For multirotors equipped with arms, the root pose error due to the floating body affects the precision at the end effector. Fixed-root approaches, such as perching on surfaces using the rotor suction force, are useful to address this problem. Furthermore, it is difficult for arm-equipped multirotors to generate large wrenches at the end effector owing to joint torque limitations. For multilink aerial robots with rotors distributed to each link, the thrust of rotors can produce large torques. Therefore, such multirotor robots can generate comparatively large wrenches at the end effector. In this paper, we introduce a rotor-distributed multilink robot that can perch on surfaces. First, we designed a root footplate and arm module for a multilink aerial robot. During perching, the joint between these two links can be passive to prevent peeling. Second, we propose a quadratic programming (QP) based controller to calculate the desired thrust for perching motion, considering the static friction and zero moment point (ZMP) conditions on the footplate. Finally, we conducted root-body perching motion tests. The manipulations of the multilink aerial robot during perching become more accurate than those during flight because the root position adheres to the environment.
GCN-Int: A Click-Through Rate Prediction Model Based on Graph Convolutional Network Interaction

Recommendation system has drawn growing attention in the academia and industry because it can solve the problem of information overload. Among a variety of methods, the click-through rate prediction model plays an important role in predicting user’s attention to a specific item. To predict click-through rate, high-dimensional and sparse features are usually adopted, and the accuracy of the prediction result depends on the combination of high-order features to a great extent. Therefore, many methods have been proposed to find the low-dimensional representation from sparse high-dimensional original features, and the meaningful way of feature combination has also been mined to improve the accuracy of the model. However, the click-through rate prediction models generally have two problems. One is that they can’t extract the feature interaction of non-Euclidean features very well. Another one is that it is hard to explain the inward meaning of feature interaction. In this paper, a GCN-int model based on the interaction of Graph Convolutional Network is proposed to solve the above problems. The proposed model simplifies the complex interaction among multiple features, gets a better representation of the interaction between high-order features, and improves the interpretability of feature interaction. The experimental results on the public movie recommendation dataset and our own IPTV movie recommendation dataset show that the proposed GCN-int model gets higher accuracy and efficiency compared with the state-of-the-art models.
CSM: Contact Sensitivity Maps for Benchmarking Robot Collision Handling Systems

In physical human-robot interaction (pHRI), robots need to detect and react to intended and unintended contacts in a safe manner. Proprioceptive sensing capabilities and collision detection and identification techniques differ among commercially available robots, which means that also their sensitivity to detect dynamic collisions with the environment or the human co-worker differ. Up to now, there exists no standardized procedure for assessing the contact sensitivity of a robotic system. In this paper, we propose the concept of contact sensitivity maps (CSM), a relationship between the robot's dynamic impact properties and the reliability of its collision handling. The CSM allows the robot user to determine for which robot workspace areas and dynamic collision parameters (mass, velocity) reliable contact detection and reaction can be expected. We propose a standardized benchmarking procedure and test setup for deriving CSMs. Finally, we analyze and compare the experimental results of the Universal Robots UR10e, UR5e, and Franka Emika Panda, where we observe significant differences in contact sensitivity.
Multibranch Learning for Angiodysplasia Segmentation with Attention-Guided Networks and Domain Adaptation

As a common cause of anemia and gastrointestinal bleeding, angiodysplasia (AD) diagnosis in wireless capsule endoscopy (WCE) images is important in clinical. Current manual review requires undivided concentration of the gastroenterologists, which is laborious and time-consuming. The development of computational methods that can assist automated diagnosis of angiodysplasia is highly desirable. In this paper, we present a new approach, ADNet, for angiodysplasia segmentation using convolutional neural networks (CNNs). Compared with previous learning strategies, ADNet gains accuracy from attentionguided and domain-adversarial training via a multibranch CNN architecture. Specifically, the core branch is constructed for AD segmentation in a fully convolutional manner. Then we propose an attention module embedded in the attention branch to enhance network feature learning, which allows ADNet to focus on the most informative and AD relevant regions while processing. Furthermore, an adaptation branch is built to learn domain-invariant features by adversarial training, aiming to improve the performance when datasets are expanded while preventing the degradation induced by the variations in WCE image acquisition. ADNet is evaluated using two WCE datasets with angiodysplasia and the results show the accuracy gains we obtain, where the state-of-the-art segmentation performance on the public dataset of GIANA’17 is achieved.
Modeling and Evaluating Epidemic Control Strategies With High-Order Temporal Networks

Non-Pharmaceutical Interventions (NPIs) are essential measures that reduce and control a severe outbreak or a pandemic, especially in the absence of drug treatments. However, estimating and evaluating their impact on society remains challenging, considering the numerous and closely tied aspects to examine. This article proposes a fine-grain modeling methodology for NPIs, based on high-order relationships between people and environments, mimicking direct and indirect contagion pathways over time. After assessing the ability of each intervention in controlling an epidemic propagation, we devise a multi-objective optimization framework, which, based on the epidemiological data, calculates the NPI combination that should be implemented to minimize the spread of an epidemic as well as the damage due to the intervention. Each intervention is thus evaluated through an agent-based simulation, considering not only the reduction in the fraction of infected but also to what extent its application damages the daily life of the population. We run experiments on three data sets, and the results illustrate how the application of NPIs should be tailored to the specific epidemic situation. They further highlight the critical importance of correctly implementing personal protective (e.g., using face masks) and sanitization measures to slow down a pathogen spreading, especially in crowded places.
Generalized Point Set Registration with the Kent Distribution

Point set registration (PSR) is an essential problem in communities of computer vision, medical robotics and biomedical engineering. This paper is motivated by considering the anisotropic characteristics of the error values in estimating both the positional and orientational vectors from the PSs to be registered. To do this, the multi-variate Gaussian and Kent distributions are utilized to model the positional and orientational uncertainties, respectively. Our contributions of this paper are three-folds: (i) the PSR problem using normal vectors is formulated as a maximum likelihood estimation (MLE) problem, where the anisotropic characteristics in both positional and normal vectors are considered; (ii) the matrix forms of the objective function and its associated gradients with respect to the desired parameters are provided, which can facilitate the computational process; (iii) two approaches of computing the normalizing constant in the Kent distribution are compared. We verify our proposed registration method on various PSs (representing pelvis and femur bones) in computer- assisted orthopedic surgery (CAOS). Extensive experimental results demonstrate that our method outperforms the state- of-the-art methods in terms of the registration accuracy and the robustness.
On the Effect of Robotic Leg Design on Energy Efficiency

In this paper, we study the effect of alternative leg designs on energy consumption in legged locomotion. Focusing on gaits with constant horizontal velocity and constant height we introduce models of two simplified parallel and serial designs with realistic mechanical and actuation parameters. The analysis yields the distribution of power demands in the leg workspace, leading to useful conclusions related to mechanical power antagonism and actuator electric losses. Mechanical antagonism occurs not only in parallel but also in serial legs causing extensive power waste, since one actuator contributes to the locomotion task and the other consumes power with no contribution to it. Based on the analysis, we propose a new leg design that minimizes the total actuation power consumption criterion given a nominal robot toe trajectory.
A Lane-Level Road Marking Map Using a Monocular Camera

The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings (RMs). Obviously, we can build the lane-level map by running a mobile mapping system (MMS) which is equipped with a high-end 3D LiDAR and a number of high-cost sensors. This approach, however, is highly expensive and ineffective since a single high-end MMS must visit every place for mapping. In this paper, a lane-level RM mapping system using a monocular camera is developed. The developed system can be considered as an alternative to expensive high-end MMS. The developed RM map includes the information of road lanes (RLs) and symbolic road markings (SRMs). First, to build a lane-level RM map, the RMs are segmented at pixel level through the deep learning network. The network is named RMNet. The segmented RMs are then gathered to build a lane-level RM map. Second, the lane-level map is improved through loop-closure detection and graph optimization. To train the RMNet and build a lane-level RM map, a new dataset named SeRM set is developed. The set is a large dataset for lane-level RM mapping and it includes a total of 25157 pixel-wise annotated images and 21000 position labeled images. Finally, the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation.
On the Modelling of the Quasi-Optical Coupling between Two Photoconductive Antennas under Pulsed Laser Illumination

Photoconductive antennas (PCA) are promising candidates for high-resolution imaging applications. They have been investigated in transmission when the receiver is a power detector. Here we consider another PCA as the receiver and develop a model of the quasi-optical (QO) coupling between the transmitter and receiver under pulsed laser illumination. Specifically, a field matching technique is used to evaluate the open-circuit voltage of the receiver. Based on this voltage, the spectral mutual coupling between the two PCAs, including the QO path can be evaluated. A bow-tie antenna feed is used as an example to show the coupling coefficient.
Unsupervised Tensor based Feature Extraction and Outlier Detection for Multivariate Time Series

Although finding useful feature vector representation is one of crucial tasks as data analysis for multivariate time series, finding useful features is still challenging because both time-wise and variable-wise associations should be taken into account. To overcome this issue, we present an unsupervised feature extraction algorithm for multivariate time series, called UFEKT (Unsupervised Feature Extraction using Kernel Method and Tucker Decomposition). Our algorithm (1) constructs a kernel matrix from subsequences of each time series to account for time-wise association and (2) constructs a single tensor from the kernel matrices and performs Tucker decomposition to account for variable-wise association. Feature representation is obtained as rows of the factor matrix of the decomposed tensor in a fully unsupervised manner, which can be used to subsequent machine learning problems. Our experimental results using synthetic and real-world multivariate time series datasets in the unsupervised outlier detection scenario show that our algorithm improves detection accuracy when it is used as pre-processing for outlier detection algorithms.
Calibration-Free Gas Quantification through Wavelength Modulation Spectroscopy in the Millimeter-Wave/Terahertz Range

Millimeter-Wave/Terahertz (mmW/THz) spectroscopy affords high precision and specificity, useful for analyzing complex gas mixtures. In conjunction with wavelength modulation spectroscopy (WMS), it stands out as a sensitive technique for trace gas detection. The complex nature of the WMS absorption line shape, however, remains challenging to quantify and interpret. In this work, we propose a practical procedure for processing and analyzing the WMS signal to derive a calibrationfree quantification of the target gas. To validate the method, we compared the results to reference measurements of methanol and water and found a good agreement.
Siamese Spectral Attention With Channel Consistency for Hyperspectral Image Classification

Abundant spectral features are the precious wealth of hyperspectral images (HSI). Nevertheless, well-designed spectral feature is still a challenge that affects the performance of the classifier, especially with insufficient number of training samples. To make up the poor discriminability of spectral feature, double-branch methods are proposed by fusing parallel spectral and spatial branches. However, this structure does nothing to improve the quality of spectral feature, which is regarded as the most valuable information for HSI information. In this article, we propose a siamese spectral attention network with channel consistency (SSACC) to focus on obtaining discriminative spectral features, thus improving the generalization ability of the classifier. Two kinds of HSI cubes with different patch sizes are generated as the input of SSACC. The two cubes are divided into top and bottom branches and then be fed into the siamese network to obtain the refined spectral features. Then, self-attention is conducted to interacting with each channel for the spectral features enhancement. Meanwhile, two attention maps are obtained to display the spectral structures of each branch. A channel consistency regularization is performed on the two attention maps by enforcing the two branches to possess similar spectral patterns when identifying the same centric pixel. Extensive experiments conducted on the three HSI datasets verify the superiority of the obtained spectral feature. Furthermore, the proposed method applying convolution only on the spectral domain outperforms the state-of-the-art double-branch methods which integrate the spectral and spatial features simultaneously.
An Improved Impedance/Admittance Analysis Method Considering Collector Subsystem Transformation in Converter-Integrated Power Systems

To analyze oscillation phenomenon caused by the interaction between converters and an AC grid, the impedance/admittance analysis method is a popular solution, in which each electrical component is represented by an external impedance/admittance matrix and a lumped matrix is formed for stability analysis. However, for converter-integrated power systems considering typical collector subsystems (e.g., the radial and the ring collector subsystems in wind farms and PV stations), the impedance network reduction to form the lumped impedance/admittance matrix (LIM/LAM) becomes complicated particularly for a ring collector subsystem, which affects the efficiency of the oscillation analysis. To address this problem, this paper proposes a method that can transform a typical topology of collector subsystems into a pure-parallel topology so that lumped admittance matrix is much easier to be derived. Algorithm complexity analysis confirms its computational advantages. A fifty-converter system is used to verify the effectiveness of the proposed method.
Non-invasive I2C Hardware Trojan Attack Vector

In smartphones, and more generally in IoT devices, manufacturers focus their efforts on securing communications with the outside world that are more exposed to attack while considering communications between secure components. By doing this, it results in internal communication buses with little or no security against attackers. I2C is the most used internal communication bus in IoT devices to communicate with sensors and memories. It is also used in recent smartphones to connect the Trusted Execution Environments (ARM TrustZone, Apple SEP, or Google Titan M) to a dedicated EEPROM memory that contains secret information such as encryption keys, anti-replay counter, or the boot ROM. In this paper, we propose a non-invasive attack through a hardware trojan on the I2C bus, which will allow us to perform two attack scenarios: a heart bleeding type attack which will allow retrieving additional information at each memory read, and a buffer overflow attack which will allow writing additional data in the memory at each write which can result in modifying secret information such as password or counters. These attacks can be performed on any device using the I2C bus. In the context of smartphones, these attacks will allow the extraction of sensitive information stored in the secure EEPROM memory.
A Self-Supervised Near-to-Far Approach for Terrain-Adaptive Off-Road Autonomous Driving

We introduce a self-supervised method for systematically choosing traversable terrain while autonomously navigating a vehicle to a goal position in an unknown off-road environment. Leveraging the color discriminant bias of off-road terrain types, and using images from a vehicle-mounted camera, we employ a viewpoint transformation that maintains the spatial layout of the terrain to cluster terrain types by color and register corresponding traversability features to guide future navigation decisions. As it navigates, our algorithm also generates training images for use in contemporary end-to-end navigation schemes. Our test results demonstrate the advantages of our approach over classical near-to-far approaches in off-road environments with unknown traversability characteristics, and highlight its fit to supervised semantic segmentation schemes that require foreknowledge of traversability characteristics for labeling, which are limited by insufficient data and suffer pixel-level class imbalance. We detail the techniques for clustering, feature registration, path planning and navigation; and demonstrate the method. Finally, we study the effectiveness of non-discretionary self-supervised data labeling.
High Misalignment Tolerance in Efficiency of WPT System with Movable Intermediate Coil and Adjustable Frequency

Offsets between the primary and secondary coils of loosely coupled transformers (LCT) are attributable to the efficiency decline of the wireless power transfer (WPT) system. To improve the misalignment tolerance of efficiency in WPT system, this paper presents a LCT system with a movable intermediate coil and adjustable system frequency, which can promote the efficiency of the WPT system under misalignment condition. First, the influences of the position and compensation parameter of intermediate coil on the system efficiency during migration are summarized. The optimal compensation parameter and optimal position selection method of intermediate coil are proposed. Then, the influence of frequency on system efficiency is studied, and the detailed control strategy of intermediate coil’s position and system frequency is proposed. A 3-kW prototype WPT with the proposed three-coil LCT is manufactured and experimental validations are also performed. The results show that the efficiency declines of three-coil LCT with the proposed control strategy is 1.8% when the lateral offsets reach 300mm, namely 43% of the outer diameter of coil.
Adaptive Channel Hopping for IEEE 802.15.4 TSCH-Based Networks: A Dynamic Bernoulli Bandit Approach

In IEEE 802.15. 4 standard for low-power low-range wireless communications, only one channel is employed for transmission which can result in increased energy consumption, high network delay and poor packet delivery ratio (PDR). In the subsequent IEEE 802.15. 4-2015 standard, a Time-slotted Channel Hopping (TSCH) mechanism has been developed which allows for a periodic yet fixed frequency hopping pattern over 16 different channels. Unfortunately, however, most of these channels are susceptible to high-power coexisting Wi-Fi signal interference and to possibly some other ISM-band transmissions. This interference manifests itself in the form of the presence/absence of other devices with either or both static and dynamic channel selection policies. In order to isolate channels with undesirable conditions, blacklisting mechanisms are defined to adapt the channel hopping process. However, the existing solutions which form blacklists unrealistically assume that the statistical model of the external interference remains fixed, and do not vary over time. In this paper, we realistically assume that the impact of external interferes on 802.15. 4 may generally follow a non-stationary pattern, and accordingly formulate the adaptive channel hopping problem as a Dynamic Multi-Armed Bernoulli Bandit (Dynamic MABB) process from the machine learning theory. We then propose an online learning algorithm with track-ability properties for computing an adaptive hopping policy. Simulations confirm that when the statistics of the external interference has a switching regime, the proposed solution outperforms the previous schemes in terms of both energy efficiency as well as two important KPIs for TSCH-based networks, i.e., PDR and latency.
Material Investigation on Radio Frequency Transparency and Thermal Stress Relaxation

An investigation on thermally resistant and radio frequency (RF) transparent materials was performed. Thermal stress relaxation tests from 20°C to 150°C showed that polystyrene foam board and polycarbonate demonstrate signs of creep deformation over time. Polyurethane foam demonstrated no thermal stress relaxation. For RF penetrability testing, the 100 MHz to 40 GHz range was studied using an outdoor test setup. Open air measurements of two collinearly faced antennas were compared to measurements taken when material samples were placed between the two antennas. This demonstrated that rigid polyurethane foam, Teflon polytetrafluoroethylene (PTFE) and borosilicate glass are RF penetrable.