Miracle-dz/UniFAR-FTUs huggingface dataset:
Deformation and Refined Features Based Lesion Detection on Chest X-Ray

Automatic and accurate detection of chest X-ray lesion is a challenging task. In the chest X-ray image, the lesions are shown with blurred boundary contours, different sizes, variable shapes, uneven density, etc. Besides, the deep convolutional neural network (CNN) consists of traditional convolution units, which has the limitations of rectangular sampling. The CNN extracts difficultly the deformation and refined features of chest X-ray lesions. Because of these factors, the accuracy of the lesion detection algorithm is not high. To deal with problems, we propose the deformation and refined features based lesion detection on the chest X-ray algorithm called DRCXNet. Firstly, the deformable convolution with amplitude modulation (AMDCN) is built to extract the deformation features of the lesions on the chest X-ray. Secondly, to obtain the refined feature, the global features and local features are fused, which can enrich the feature space of the lesion. Thirdly, the pooling layer combines with the AMDCN and region proposal network to establish the deformable pooling layer, which enhances the model’s sensitivity to the lesion location. During the training, the model is optimized by the improved regression loss function with a gradient control factor. On the public datasets RSNA and ChestX-ray8, the proposed method outperforms seven popular detection algorithms. The proposed method is a significant performance in both qualitative and quantitative experiments. Its comprehensive evaluation scores, sensitivity, precision, and the mean dice similarity coefficient are 0.866, 0.914, 0.836 and 0.859 respectively. The proposed algorithm achieves a very satisfactory result.
Agile Standing-up Control of Humanoids: Energy-based Reactive Contact Wrench Optimization with Strict Dynamic Consistency

This paper presents a dynamic whole-body control method for humanoids to render agile and stable standing-up motion based on energy concepts. First, to cope with the standing-up problem with multiple contacts in hierarchical tasks, an enhanced operational-space based whole-body control (WBC) framework is proposed, which offers optimal torque resolutions guaranteeing strict dynamic consistency with inequality constraints formulated by quadratic programming. Second, agile standing-up control strategy with dynamic push and rise actions is newly developed based on the notion of the total energy. The optimal pushing wrenches at contacts are computed to obtain sufficient energy to accelerate the center-of-mass (CoM) of the robot as quick as possible, and the total energy is then controlled to attain rapid rise-up motion and to stabilize the body of the robot. Consequently, the robot can effectively and actively stand up to recover from a certain pose in which cannot be accomplished by any quasi-static motion. The proposed method is numerically experimented and validated with dynamic parameters from the real humanoid COMAN+, fulfilling different types of standing-up actions.
Intelligent Routing based on Deep Reinforcement Learning in Software-Defined Data-Center Networks

In software-defined data-center networks, Elephant flow/Mice flow/Coflow coexist and multiple resources (bandwidth, cache and computing) coexist. However, the conventional routing methods cannot overcome the large gap between different performance requirements of flow and efficient resource allocation. Therefore, this paper proposes DRL-R (Deep Reinforcement Learning-based Routing) to bridge this gap. First, we recombine multiple resources (node’s cache, link’s bandwidth) by quantifying the contribution score of them reducing the delay. This actually converts the performance requirements of flow into resource requirements of this flow, hence, the routing problem can be converted into a job-scheduling problem in resource management. Second, a DRL agent deployed on an SDN controller continually interacts with the network for adaptively performing reasonable routing according to the network state, and optimally allocating network resources for traffic. We employ Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) to build the DRL-R. Finally, we demonstrate the effectiveness of DRL-R through extensive simulations. Benefitted from continually learning with a global view, DRL-R can improve throughput highest up to 40% and flow completion time highest up to 47% over OSPF. DRL-R can improve the load balance of link highest up to 18.8% over OSPF. Additionally, DDPG has better performance than DQN.
End-to-End Driving Model for Steering Control of Autonomous Vehicles with Future Spatiotemporal Features

End-to-end deep learning has gained considerable interests in autonomous driving vehicles in both academic and industrial fields, especially in decision making process. One critical issue in decision making process of autonomous driving vehicles is steering control. Researchers has already trained different artificial neural networks to predict steering angle with front-facing camera data stream. However, existing end-to-end methods only consider the spatiotemporal relation on a single layer and lack the ability of extracting future spatiotemporal information. In this paper, we propose an end-to-end driving model based on Convolutional Long Short-Term Memory (Conv-LSTM) neural network with a Multi-scale Spatiotemporal Integration (MSI) module, which aiming to encode the spatiotemporal information from different scales for steering angle prediction. Moreover, we employ future sequential information to enhance spatiotemporal features of the end-to-end driving model. We demonstrate the efficiency of proposed end-to-end driving model on the public Udacity dataset with comparison of some existing methods. Experimental results show that the proposed model has better performances than other existing methods, especially in some complex scenarios. Furthermore, we evaluate the proposed driving model on a real-time autonomous vehicle, and results show that the proposed driving model is able to predict the steering angle with high accuracy compared to skilled human driver.
3D Point Cloud Data Acquisition Using a Synchronized In-Air Imaging Sonar Sensor Network

Obtaining accurate data about the environment in which a robot is located is a crucial matter when it comes to autonomous navigation and other robotic applications. A popular method of acquiring this information is to use sonar-rings, where a robot is fitted with multiple simple ultrasound transducers pointed in the directions where an object can appear. However, in a time where accurate 3D data is gaining importance, other sensing modalities are becoming more popular because of the ability to measure dense 3D point clouds. In these point clouds not only the horizontal plane is measured, but objects in the elevation planes can also be registered, which can be very interesting and makes applications such as 3D SLAM or object recognition possible. In this paper we present a way to extract complex 3D point cloud data from the entire surrounding sphere using multiple interconnected eRTIS sensors. These advanced imaging sonar sensors offer the flexibility of the popular sonar-ring in combination with the benefits of some of the competing sensing modalities. The setup presented here uses less sonar sensors (and thus less external hardware) while obtaining more information from the complete frontal hemispheres of each individual sensor. This setup is discussed, along with the issues that arise when using complex imaging sonar sensors in a network, and is tested in an indoor and outdoor environment. At the end of this paper is a discussion of the obtained results.
Similarity detection for smart and transparent long-range IoT relaying

LPWAN refers to highly energy-efficient wireless communication over very long distances. Nonetheless, even with the increased range, 1-hop connectivity can be difficult to achieve in real-world deployment scenario, especially for remote and rural areas where density of gateways is low and where devices/gateway are usually deployed for a specific application. Therefore a smart and transparent 2-hop approach has been proposed in a previous work to leverage these connectivity issues. This article extents this approach with similarity detection features in order to (i) reduce the power consumption when waking-up to relay packets and (ii) reduce the radio activity time when running under duty-cycle regulated constraints.
Effects of Limb Morphology on Transient Locomotion in Quadruped Robots

The natural world hosts a few mammals that display both a high degree of agility and speed. However these animals have complex leg morphologies. Robot designers are thus faced with the dilemma of which morphology to employ when designing the next generation of agile legged robots. Thus this letter presents a novel investigation into the effects of limb morphology of quadrupeds during rapid transient maneuvers such as acceleration and deceleration. Three leg configurations inspired by nature (O-, X-Type and All-Ankle) as well as All-Knee configurations are compared. Extensive large scale Monte Carlo simulations utilizing contact-implicit trajectory optimization methods were employed on 100 randomly generated robots of varying sizes to determine the optimal configuration for the task of rapid transient locomotion. After extensive analysis, the investigation revealed that an X-Type leg configuration outperformed all other configurations. Ultimately, these results will provide insight for the mechanical design of future agile quadruped robots.
Fusion of fluxgate sensors with navigational data for the improvement of the detection of underwater metal-containing objects

With the underwater human activity actively developing and the rise of the autonomous unmanned underwater vehicles (AUV) industry, researchers and engineers face the task of maintenance and repair of communications. For this purpose AUVs can be used, with e.g. the task of inspecting pipelines. The article describes the processing algorithm for the signal from passive ferromagnetic (fluxgate) sensors mounted on an AUV carrier used to search for metal-containing objects at the sea bottom. A scheme for such a measurement is proposed - the installation of two sensors at the opposite ends of the carrier. This allows to measure the gradient of magnetic field between the sensors. The characteristic form of such a signal and the dependence of the signal on the motion parameters of the vehicle and external factors are determined. To eliminate false positives, filters are used based on the readings of the position, speed and orientation sensors of the navigation system. Using data on the motion parameters of the device allows to generate a reference signal, which is used to validate the detection of an object using the cross-correlation method. The use of data on orientation angles makes it possible to compensate for the influence of the orientation of the device in the Earths magnetic field.
Eigen-Factors: Plane Estimation for Multi-Frame and Time-Continuous Point Cloud Alignment

In this paper, we introduce the Eigen-Factor (EF) method, which estimates a planar surface from a set of point clouds (PCs), with the peculiarity that these points have been observed from different poses, i.e. the trajectory described by a sensor. We propose to use multiple Eigen-Factors (EFs) or different planes’ estimations, that allow to solve the multi-frame alignment over a sequence of observed PCs. Moreover, the complexity of the EFs optimization is independent of the number of points, but depends on the number of planes and poses. To achieve this, a closed-form of the gradient is derived by differentiating over the minimum eigenvalue with respect to poses, hence the name Eigen-Factor. In addition, a time-continuous trajectory version of EFs is proposed. The EFs approach is evaluated on a simulated environment and compared with two variants of ICP, showing that it is possible to optimize over all point errors, improving both the accuracy and computational time. Code has been made publicly available.
Brain Activity Recognition of Chinese Character Processing Based on Functional Magnetic Resonance Image

Brain activity recognition based on functional magnetic resonance image (fMRI) as a vital problem in computer vision has been applied to many fields. Chinese character processing is one of the most remarkable applications of brain activity recognition at present, it has shown great performance using activation data and cognitive experiment in different brain regions. Inspired by new machine learning methods, this paper proposes an extraction method of human brain data with region of interest (ROI) and a activity recognition method of classifying Chinese character processing with support vector machine (SVM) and random forest (RF). By comparison, the SVM approach and RF approach achieve the average correct classification rate of Left Inferior Frontal Gyrus (LIFG) region of human brain with 74.1% and 80.5% after the ROI extraction, respectively. The experiments indicate that the brain's processing mechanism does differ when facing the Chinese characters of different structures, the LIFG brain region plays an important role in this process, and the RF approach would achieve better performance than SVM in term of average correct classification rate.
Online Relative Footstep Optimization for Legged Robots Dynamic Walking Using Discrete-Time Model Predictive Control

We present a unified control framework that generates dynamic walking motions for biped and quadruped robots with online relative footstep optimization. The footstep optimization is formulated as a discrete-time Model Predictive Control problem which determines future footstep locations. The framework has a hierarchical structure consisting of three layers: footstep planner, trajectory generator and whole-body controller. The footstep planner plans next footstep position based on Linear Inverted Pendulum (LIP) model. Relative footstep optimization is proposed to enable automatic footstep planning without the use of any predefined footstep sequences. The trajectory generator will generate CoM and feet trajectory given the next footstep placement. In order to generalize to quadruped robots, “virtual leg” concept has been used to coordinate leg pair movement. The whole-body inverse dynamic controller calculates joint torques to track given Cartesian reference trajectories. To include under-actuation into consideration, contact vertices formulation of ground reaction forces (GRFs) has been adopted. Generalized whole-body controller can handle biped robot with line feet as well as quadruped robots with point feet walking with dynamic gaits. Several simulations have been performed to demonstrate the robustness and generality of the proposed framework.
Search Text to Retrieve Graphs: A Scalable RDF Keyword-Based Search System

Keyword-based access to structured data has been gaining traction both in research and industry as a means to facilitate access to information. In recent years, the research community and big data technology vendors have put much effort into developing new approaches for keyword search over structured data. Accessing these data through structured query languages, such as SQL or SPARQL, can be hard for end-users accustomed to Web-based search systems. To overcome this issue, keyword search in databases is becoming the technology of choice, although its efficiency and effectiveness problems still prevent a large scale diffusion. In this work, we focus on graph data, and we propose the TSA+BM25 and the TSA+VDP keyword search systems over RDF datasets based on the “virtual documents” approach. This approach enables high scalability because it moves most of the computational complexity off-line and then exploits highly efficient text retrieval techniques and data structures to carry out the on-line phase. Nevertheless, text retrieval techniques scale well to large datasets but need to be adapted to the complexity of structured data. The new approaches we propose are more efficient and effective compared to state-of-the-art systems. In particular, we show that our systems scale to work with RDF datasets composed of hundreds of millions of triples and obtain competitive results in terms of effectiveness.
Operational Space Control Framework for Torque Controlled Humanoid Robots with Joint Elasticity

Torque controlled robots have the capability of implementing compliant behavior with back-drivability. In practice, however, joint elasticity often prevents an accurate position tracking performance of a robot. In particular, humanoid robots are influenced more by elasticity because of a long kinematic chain between the feet and hands. In this paper, we present a new inverse dynamics based control approach for torque controlled humanoid robots with joint elasticity. When formulating the operational space control framework, feedback control consists of only motor-related parts with measured motor angle values, and the link dynamics is compensated by the feedforward terms. The experiment results of the proposed approach show a noticeable improvement in the position tracking performance in 6-DoF manipulator. Finally, the proposed method was applied to a torque controlled biped robot for walking. Both stiff motion control of the CoM and compliant motion control of the foot were simultaneously achieved, demonstrating the advantage of the torque controlled robot.
On the effect of semielliptical foot shape on the energetic efficiency of passive bipedal gait*

This paper studies the effects of varying rollover curvature on the passive dynamic gait of a biped walker. The dynamic model of a compliant biped robot is extended with the implementation of semielliptical feet, to mimic human rollingradius progression during a step. The process of modeling the semielliptical foot shape and integrating its kinematics to the biped’s dynamics is presented in detail. The passive dynamic behavior of the biped for elliptic feet of various dimensions is investigated through numerical simulations to provide results about gait stability, walking speed, energetic efficiency, and impact force levels. The concept of energetic efficiency in passive walking is discussed thoroughly, and an efficiency comparison methodology is proposed. Finally, it is shown that the biomimetically-inspired semielliptical foot profile can lead to higher gait efficiency. The results of this study can be used to optimize energetic efficiency in biped walking machines and/or gait assisting prosthetic equipment by means of foot shape optimization.
Interdecadal Changes of Characteristics of Tropical Cyclone Rapid Intensification Over Western North Pacific

This study investigates the interdecadal variations of rapid intensification (RI) of tropical cyclones (TCs) over Western North Pacific (WNP) during 1960–2016, TC variabilities associated with RI and the possible mechanisms for such variations are also explored. Two RI-active periods (P1:1960–1972, P3:2000–2016) and one RI-inactive period (P2:1973–1999) are identified using Regime Shift Detection method based on the RI-TC ratio data. RI spatial distributions all exhibit a common Main Development Region. Significant different characteristics of the TC genesis and lifetime maximum intensity location are observed between RITCs and Non-RI TCs and in each period. The majority of RITCs starts with initial intensity at 35–80 kts, and P2 shows an obvious displacement of these TC occurrences. The longitudinal distributions of RI ratio demonstrate totally different patterns. The higher SST and relative humidity, as well as stronger VWS, lead to the highest RI ratio in P3. TCs in P2 have less chance to undergo RI due to the eastward extension of monsoon trough associated with a more eastward movement of TC occurrence away from MDR. The environmental conditions are obviously unfavorable for RI in the Western WNP in P2, as well as in P1. Our findings imply that the TC variabilities are affected by the decadal variations of RI events and the changes in TC occurrence along with the RI-favored environmental fields all contribute to the RI variations at interdecadal scales.
Task-specific Self-body Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving

The musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex flexible body is difficult. Although we have developed an online acquisition method of the nonlinear relationship between joints and muscles, we could not completely match the actual robot and its self-body image. When realizing a certain task, the direct relationship between the control input and task state needs to be learned. So, we construct a neural network representing the time-series relationship between the control input and task state, and realize the intended task state by applying the network to a real-time control. In this research, we conduct accelerator pedal control experiments as one application, and verify the effectiveness of this study.
Rumor Propagation: A State-of-the-art Survey of Current Challenges and Opportunities

Rumor propagation is an alarming problem that creates a lot of predicament and has significant impact on people’s lives. Earlier rumor could only be spread by the word of the mouth however in the current Web age, people are using electronics much more than they used to before, thereby resulting into lot of social interaction on the Web and hence spread of fake news is at its apex. This survey paper is written while keeping in mind the problem of fake news propagation and various approaches given to limit the spread up to a considerable extent. Fake news propagation is a new field of research and constant work is going on this field. Various models already given attempt to recognize the pattern of the news spread, correlates the rumor propagation with nature inspired phenomena. In this review paper, we also give the comparison between these models and their variants, this study leads us to see which areas are still challenging and what are the future prospects of rumor propagation.
Efficient Optical Modulation With High Data-Rate in Silicon Based Laterally Split Vertical p-n Junction

Efficient optical modulation along with high data rate is a prerequisite for the realization of high-performance optical modulator. Optical modulation in silicon with high data-rate and high modulation efficiency is proposed by a laterally separated vertical p-n junction. Two independent but synchronized p-n junctions that supports the common optical mode are created by forming a slot waveguide structure. It provides a prominent way to enhance the light-material interaction necessarily to achieve low $\text{V}_{\pi }\text{L}$ along with the low RC time constant which is crucial for the high-speed operation. The proposed device shows a high modulation efficiency of 0.74 V-cm for 1.2 mm long device. The calculated intrinsic 3-dB bandwidth reaches up to ≈ 58 GHz at a reverse bias of 6 V. We show high speed operation up to 25 Gbit/s for the device length of $600~\mu \text{m}$ with a simple lumped electrode configuration. The Traveling-wave electrodes as a coplanar waveguide is employed to further improve the speed performance of the device. By taking the advantage of excellent velocity matching between optical group index and RF effective index, high data rate performance up to 100 Gbit/s is obtained with an extinction ratio of 2.4 dB. The proposed device opens new avenues for high speed optical interconnect on an SOI platform.