Miracle-dz/UniFAR-FTUs huggingface dataset:
Extrinsic and Temporal Calibration of Automotive Radar and 3D LiDAR

While automotive radars are widely used in most assisted and autonomous driving systems, only a few works were proposed to tackle the calibration problems of automotive radars with other perception sensors. One of the key calibration challenges of automotive planar radars with other sensors is the missing elevation angle in 3D space. In this paper, extrinsic calibration is accomplished based on the observation that the radar cross section (RCS) measurements have different value distributions across radar’s vertical field of view. An approach to accurately and efficiently estimate the time delay between radars and LiDARs based on spatial-temporal relationships of calibration target positions is proposed. In addition, a localization method for calibration target detection and localization in pre-built maps is proposed to tackle insufficient LiDAR measurements on calibration targets. The experimental results show the feasibility and effectiveness of the proposed Radar-LiDAR extrinsic and temporal calibration approaches.
Remove, then Revert: Static Point cloud Map Construction using Multiresolution Range Images

We present a novel static point cloud map construction algorithm, called Removert, for use within dynamic urban environments. Leaving only static points and excluding dynamic objects is a critical problem in various robust robot missions in changing outdoors, and the procedure commonly contains comparing a query to the noisy map that has dynamic points. In doing so, however, the estimated discrepancies between a query scan and the noisy map tend to possess errors due to imperfect pose estimation, which degrades the static map quality. To tackle the problem, we propose a multiresolution range image-based false prediction reverting algorithm. We first conservatively retain definite static points and iteratively recover more uncertain static points by enlarging the query-to- map association window size, which implicitly compensates the LiDAR motion or registration errors. We validate our method on the KITTI dataset using SemanticKITTI as ground truth, and show our method qualitatively competes or outperforms the human-labeled data (SemanticKITTI) in ambiguous regions.
Analysis of Far-Field Scattering and Near-Field Coupling of a Target above Rough Surface Using DD-FDTD

A domain decomposition-finite difference time domain (DD-FDTD) method analyzing the far-field scattering and near-field coupling of a target above the rough surface is presented to reduce the computational burden. The domain decomposition scheme is introduced to place the target and the rough surface into two FDTD domains referred to as the target subdomain and the rough surface subdomain. The field continuity of the target subdomain is ensured by injecting the reflected wave from the rough surface into it. The reflected wave is computed by running the finite difference time domain (FDTD) in the rough surface subdomain and by transforming the near field according the Green’s function. As a result, a large number of meshes between target and rough surface is removed, so the computational burden is reduced in the FDTD method. The numerical results compared with the conventional FDTD method show that the DD-FDTD is more efficient, especially when the distance between the target and rough surface becomes larger.
Revert Propagation: Who are responsible for a contagion initialization in a Diffusion Network?

Millions of stories are transferred in a social network and some of them are malicious. Can we identify the source node(s) that are responsible to initiate the propagation originally? If so, when did they initiated the propagation? The problem of identifying the source of propagation based on limited observations has been studied significantly in recent years, as it can help to reduce the damage caused by unwanted infections with early detection. In this paper, we present an efficient algorithm for finding a node initiating a piece of information into the network and also inferring the time when it is initiated. We propose Source Location Estimation method, SoLE, that estimate the source probability for each node and then choose the source set which are maximize the probability using a well-known greedy method with a theoretical guarantees. The Observed nodes are detected nodes which are known clearly that spread specified malicious information in the network but small fraction of nodes are detected. The Hidden infected nodes are hidden, which spread the information in the network, however, they're not identified yet. In this problem, we first estimate the shortest path between other nodes to observed ones for each propagation trace, SoLE. Afterward, we find the best nodes as the source set among the hidden nodes by optimizing a loss function. Our experiments on real-world propagation through networks show the superiority of our approach in detecting true sources and promote the top ten accuracy from less than 10% for the state-of-the-art methods to approximately 30%.
Effect of precipitation inhibitors on calcium phosphate scale formation

The highly reproducible pH-stat technique was used to study the influence of various types of inhibitors on the precipitation of calcium phosphate from solution. Three types of inhibitors were evaluated: (1) homo- and copolymers of acrylic acid, (2) polyphosphate and phosphonates, and (3) polycarboxylic acids. Experimental variables studied included pH, temperature, and inhibitor dosage all of which were found to greatly affect the precipitation of calcium phosphate. The role of polyacrylic acid molecular weight is addressed. Comparative data for the various types of inhibitors is discussed. Keywords: precipitation inhibitors, calcium phosphate scale formation.
Model Predictive Trajectory Planning of Autonomous Vehicles Considering Dynamic Driving Constraints

A key problem to be solved for automated driving is to generate a feasible trajectory regarding the constraints imposed by vehicle physical limits and road surface. This paper presents a model predictive trajectory planning framework for automated driving, which takes the dynamic driving constraints into consideration. The constraints are calculated by a separate dynamic driving constraints computation module and represented in the form of inequalities. This module decouples the planner from the vehicle physics such that the planner only needs to consider the kinematics of the vehicle. Therefore, the planner can generate feasible trajectories without directly knowing the properties of vehicle or actuators. The proposed model predictive trajectory planning framework is evaluated in simulation with three different scenarios and the results demonstrate the effectiveness of the proposed framework even in critical use cases of automated driving vehicles.
PeCoQ: A Dataset for Persian Complex Question Answering over Knowledge Graph

Question answering systems may find the answers to users' questions from either unstructured texts or structured data such as knowledge graphs. Answering questions using supervised learning approaches including deep learning models need large training datasets. In recent years, some datasets have been presented for the task of Question answering over knowledge graphs, which is the focus of this paper. Although many datasets in English were proposed, there have been a few question answering datasets in Persian. This paper introduces PeCoQ, a dataset for Persian question answering. This dataset contains 10,000 complex questions and answers extracted from the Persian knowledge graph, FarsBase. For each question, the SPARQL query and two paraphrases that were written by linguists are provided as well. There are different types of complexities in the dataset, such as multi-relation, multi-entity, ordinal, and temporal constraints. In this paper, we discuss the dataset's characteristics and describe our methodolozv for buildinz it.
Area Efficient Nearly Accurate Approximate Adder Design

As CMOS technology is scaling down day by day to a few nanometre, there is a difficulty in improving circuit performance and energy scaling in a cost-effective manner. On the other hand, computational scaling from future workloads is increasing rapidly, because of this there is a gap between capabilities of CMOS technology scaling down and the requirement for future application workloads. In such scenario, either we have to accept computing systems that are good enough or look for alternative modifications to advance them without technological progress. There are several approaches, which can reduce this gap by improving system capabilities. Approximate computing is one of them. It is now becoming an interesting area for energy efficiency since computing-intensive applications such as visual processing, multimedia signal does not require accuracy to work correctly. In this paper, an approximate adder is proposed which is nearly equal to its accurate counterpart in terms of delay and accuracy. And have improvement in terms, power, and area over 50% when compared to conventional adder's implementation and 10% when compared to existing ones.
Simulation-Based Power-Loss Optimization of General-Purpose High-Voltage SiC MOSFET Circuit Under High-Frequency Operation

This investigation focuses on a simulation-based power-loss optimization scheme, which is applied to laboratory developed general-purpose inverter-stack circuit, fabricated using high-voltage SiC-MOSFET. The optimization mainly addresses the external elements under fixed output current. It is found, that an increase in switching frequency causes increased loss, which is however less than that calculated by following the conventionally predicted linear dependence on the frequency. The switching loss is also strongly varying, in accordance with the output current variation, which is determined by the circuit requirements. Here, it is demonstrated, that circuit simulation with an accurate compact model can be successfully applied to realize significant improvements, leading to lower power loss under the requested operating condition. It is also found that the SiC-MOSFET can maintain the efficiency of nearly 98% even for high-frequency operations.
Development of Macroporous Titania Monoliths Using a Biocompatible Method. Part 1: Material Fabrication and Characterization

Monolithic titania could offer significant potential as a support for bioaffinity chromatography because of its stability, unlike silica, to a wide range of pH conditions and its ability to selectively bind phosphorylated proteins and peptides. However, traditional routes to monolithic titania utilize harsh conditions incompatible with most biomolecules. To address this, titania monoliths were prepared in a biocompatible sol−gel process from Ti(OiPr)4 and glycerol. Varied porosities could be introduced by the additional use of high-molecular-weight poly(ethylene oxide) in the sol, which led to the formation of two phases prior to gelation. Morphologies, including bimodal meso- and macroporous structures, and the polymerization of either the dispersed or condensed phases could be controlled by the fraction and molecular weight of PEO in the sol. The roles of glycerol and PEO are to retard hydrolysis and condensation reactions so that phase separation of titanium-rich species precedes gelation processes. PE.
Autonomous model-based assessment of mechanical failures of reconfigurable modular robots with a Conjugate Gradient solver

Large-scale 3D autonomous self-reconfigurable modular robots are made of numerous interconnected robotic modules that operate in a close packing. The modules are assumed to have their own CPU and memory, and are only able to communicate with their direct neighbors. As such, the robots embody a special computing architecture: a distributed memory and distributed CPU system with a local message-passing interface. The modules can move and rearrange themselves changing the robot's connection topology. This may potentially cause mechanical failures (e.g., overloading of some inter-modular connections), which are irreversible and need to be detected in advance. In the present contribution, we further develop the idea of performing model-based detection of mechanical failures, posed in the form of balance equations solved by the modular robot itself in a distributed manner. A special implementation of the Conjugate Gradient iterative solution method is proposed and shown to greatly reduce the required number of iterations compared with the weighted Jacobi method used previously. The algorithm is verified in a virtual test bed—the VisibleSim emulator of the modular robot. The assessments of time-, CPU-, communication- and memory complexities of the proposed scheme are provided.
Programmable Metasurface Based Microwave Gesture Detection and Recognition Using Deep Learning

Gesture is an important way of communication in people’s daily life, helping people to understand each other in some special cases. Most of recent research is of the optical field, which is limited when in dark or with obstacles, while the microwave can address these issues with its all-day, all-weather, and penetrability properties. Programmable metasurfaces have been widely applied in many fields since it’s programmable, flexible, controllable and multifunctional. In this paper, we realize the gesture detection and recognition using deep learning neural network, Faster Region-based Conventional Neural Network (Faster R-CNN) and Conventional Neural Network (CNN). The detection and recognition here are based on the microwave images with programmable metasurface. The accuracy of gesture detection and recognition are both very high, beyond 90%.
Differentiation of Soy Sauce Produced from Whole Soybeans and Defatted Soybeans by Pattern Recognition Analysis of HPLC Profiles

Nonvolatile components in commercial fermented soy sauce produced from whole soybeans and defatted soybeans were analyzed by reversed phase HPLC (RP-HPLC). Differentiation of soy sauce produced from two types of soybeans was attempted by pattern recognition analysis of HPLC profiles. Two distinct groups were observed in both the factor score plot and the clustering dendrogram of HPLC profiles. Two soy sauce groups were clearly differentiated by soft independent modeling of class analogy (SIMCA), where ferulic acid was identified as one of the key components for the differentiation. A fractional factorial experimental design combined with multiple linear regression analysis was applied to determine components associated with types of soybeans. Daidzein and three isoflavone derivatives besides ferulic acid were identified as typical components that increased when soy sauce was produced from whole soybeans. Keywords: Soy sauce; pattern recognition; cluster analysis; SIMCA; fractional factorial design
A Time-Division-Multiplexed Clocked-Analog Low-Dropout Regulator

This paper presents a time-division-multiplexed (TDM) clocked-analog low-dropout regulator (CLDO) that shares one controller between multiple output channels. Clocked-analog operation is introduced to create idle periods that enable the shared controller to independently regulate different output channels through time-division multiplexing. Furthermore, an asynchronous transient enhancing technique is presented. Thanks to the controller sharing, the TDM CLDO is more area-efficient than conventional designs for supplying multiple outputs, especially when power stages and on-chip loads are small. To verify the effectiveness of the TDM CLDO, a dual-channel version is fabricated in a 130 nm CMOS process. Measurement results show that it can independently track two 100 kHz <inline-formula> <tex-math notation="LaTeX">$0.3~\rm V_{pp}$ </tex-math></inline-formula> sinusoidal signals with 4 mV average output error at <inline-formula> <tex-math notation="LaTeX">$100~\Omega $ </tex-math></inline-formula> load and 6 MHz clock frequency. For load transient responses, it can independently regulate two output channels to 1.05 V and 0.95 V with 41 mV/88 mV and 67 mV/39 mV overshoot/undershoot when both channels experience 5 mA current steps at <inline-formula> <tex-math notation="LaTeX">$1.2~\rm V_{dd}$ </tex-math></inline-formula>. To the best of our knowledge, this is the first time that hardware sharing is implemented for continuous closed-loop systems.
SynChrono: A Scalable, Physics-Based Simulation Platform For Testing Groups of Autonomous Vehicles and/or Robots

This contribution is concerned with the topic of using simulation to understand the behavior of groups of mutually interacting autonomous vehicles (AVs) or robots engaged in traffic/maneuvers that involve coordinated operation. We outline the structure of a multi-agent simulator called SYN-CHRONO and provide results pertaining to its scalability and ability to run real-time scenarios with humans in the loop. SYN-CHRONO is a scalable multi-agent, high-fidelity environment whose purpose is that of testing AV and robot control strategies. Four main components make up the core of the simulation platform: a physics-based dynamics engine that can simulate rigid and compliant systems, fluid-solid interactions, and deformable terrains; a module that provides sensing simulation; an agent-to-agent communication server; dynamic virtual worlds, which host the interacting agents operating in a coordinated scenario. The platform provides a virtual proving ground that can be used to answer questions such as "what will an AV do when it skids on a patch of ice and moves one way while facing the other way? "; "is a new agent-control strategy robust enough to handle unforeseen circumstances? "; and "what is the effect of a loss of communication between agents engaged in a coordinated maneuver? ". Full videos based on work in the paper are available at https://tinyurl.com/ChronoIROS2020 and additional descriptions on the particular version of software used is available at https://github.com/uwsbel/publications-data/tree/master/2020/IROS.
Emerging Technologies of Deep Learning Models Development for Pavement Temperature Prediction

Air temperature is one of the critical factors influencing the bearing ability and performance of temperature-sensitive asphalt materials. This research investigates the relationship between air temperature at different depths and time to predict asphalt pavement temperature and evaluate asphalt performance. This paper discusses four deep learning-based regression models for calculating asphalt pavement temperature based on air temperature, depth from the asphalt surface, and time. Measurement of pavement temperature was made in the Gaza Strip. Monitoring stations were set up to measure asphalt pavement temperature and air temperature at different depths and times. The data were collected by hand measurement for the period from March 2012 to February 2013. The data is trained and validated using the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU). Bi-LSTM has an R2 of 0.9555 for the generated dataset and outperforms other algorithms because of its superiority in feature extraction and multidimensional data processing. Through deep learning techniques, Bi-LSTM has demonstrated outstanding robustness and promising potential in predicting asphalt pavement temperature.
Robust Gait Synthesis Combining Constrained Optimization and Imitation Learning

Despite plenty of motion planning strategies have been proposed for bipedal locomotion, enhancing the walking robustness in real-world environments is still an open question. This paper focuses on robust body and leg trajectories synthesis through integrating constrained optimization with imitation learning. Specifically, we first propose a Quadratically Constrained Quadratic Programming (QCQP) algorithm to make use of the ankle strategy and stepping strategy. Based on the Linear Inverted Pendulum (LIP) model, body motion can be determined by the modulated Center of Pressure (CoP) position and step parameters (including step location and step duration). After that, we exploit an imitation learning approach Kernelized Movement Primitives (KMP) to plan robot leg motions, which allows for adapting the learned motion patterns to new situations (e.g., passing through various desired points) in a straightforward manner. Several LIP simulations and whole-body dynamic simulations demonstrate that higher walking robustness can be achieved using our framework.
Recurrently-Trained Super-Resolution

We are motivated by the observation that for problems where inputs and outputs are in the same form such as in image enhancement, deep neural networks can be reinforced by retraining the network using a new target set to the output for the original target. As an example, we introduce a new learning strategy for super-resolution by recurrently training the same simple network. Unlike the existing self-trained SR, which involves a single stage of learning with multiple runs at test time, our method trains the same SR network multiple times with increasingly better targets requiring only a single inference at test time. At each stage of the proposed learning scheme, a new target for training is obtained by applying the most recently trained SR network to the original image and downscaling the resultant SR image to normalize the size. Even if downscaling is involved, we argue that the downscaled SR image acts as a better target compared to the old target. We could mathematically demonstrate that this process is similar to unsharp masking when it is linearly approximated and that this process makes the image sharper. However, unlike unsharp masking, the proposed recurrent learning tends to converge to a specific target. By retraining the existing network aiming at a more enhanced target, the proposed method can achieve a similar effect of applying SR multiple times without increasing implementation cost and inference time. To objectively verify the supremacy of our approach by experiments, we propose to use VIQET MOS, which does not require a reference image as a measure of image quality. As far as we know, our work of using an objective quality measure in image enhancement is the first one whose validity was verified by showing similar results to the actual user’s subjective evaluation. The proposed recurrent learning scheme makes existing SR algorithms more useful by clearly improving the effect of SR. Codes are available at https://github.com/rtsr82/rtsr.git
Multi-Task Deep Learning for Depth-based Person Perception in Mobile Robotics

Efficient and robust person perception is one of the most basic skills a mobile robot must have to ensure intuitive human-machine interaction. In addition to person detection, this also includes estimating various attributes, like posture or body orientation, in order to achieve user-adaptive behavior. However, given limited computing and battery capabilities on a mobile robot, it is inefficient to solve all perception tasks separately, especially when using computationally expensive deep neural networks. Therefore, we propose a multi-task system for person perception, comprising of a fast, depth- based region proposal and an efficient, lightweight deep neural network. Using a single network forward pass, the system simultaneously detects persons, classifies their body postures, and estimates the upper body orientations while retaining almost the same computation time as a single-task network. We describe how to handle a real-world multi-task scenario and conduct an extensive series of experiments in order to compare various network architectures and task weightings. We further show that multi-task learning improves the networks’ performance compared to their single-task baselines. For training and evaluation, we combine an existing dataset for orientation estimation and a new, self-recorded dataset, consisting of more than 235,000 depth patches that is made publicly available to the research community.