RaPlace: Place Recognition for Imaging Radar using Radon Transform and Mutable Threshold

Due to the robustness in sensing, radar has been highlighted, overcoming harsh weather conditions such as fog and heavy snow. In this paper, we present a novel radar-only place recognition that measures the similarity score by utilizing Radon-transformed sinogram images and cross-correlation in frequency domain. Doing so achieves rigid transform invariance during place recognition, while ignoring the effects of radar multipath and ring noises. In addition, we compute the radar similarity distance using mutable threshold to mitigate variability of the similarity score, and reduce the time complexity of processing a copious radar data with hierarchical retrieval. We demonstrate the matching performance for both intra-session loop-closure detection and global place recognition using a publicly available imaging radar datasets. We verify reliable performance compared to existing stable radar place recognition method. Furthermore, codes for the proposed imaging radar place recognition is released for community

Quantum chemical studies of redox properties and conformational changes of a four-center iron CO2 reduction electrocatalyst.

The CO2 reduction electrocatalyst [Fe4N(CO)12]- (abbrev. 1-) reduces CO2 to HCO2- in a two-electron, one-proton catalytic cycle. Here, we employ ab initio calculations to estimate the first two redox potentials of 1- and explore the pathway of a side reaction involving CO dissociation from 13-. Using the BP86 density functional approximation, the redox potentials were computed with a root mean squared error of 0.15 V with respect to experimental data. High temperature Born-Oppenheimer molecular dynamics was employed to discover a reaction pathway of CO dissociation from 13- with a reaction energy of +10.6 kcal mol-1 and an activation energy of 18.8 kcal mol-1; including harmonic free energy terms, this yields ΔGsep = 1.4 kcal mol-1 for fully separated species and ΔG‡ = +17.4 kcal mol-1, indicating CO dissociation is energetically accessible at ambient conditions. The analogous dissociation pathway from 12- has a reaction energy of 22.1 kcal mol-1 and an activation energy of 22.4 kcal mol-1 (ΔGsep = 12.8 kcal mol-1, ΔG‡ = +18.1 kcal mol-1). Our computed harmonic vibrational analysis of [Fe4N(CO)11]3- or 23- reveals a distinct CO-stretching peak red-shifted from the main CO-stretching band, pointing to a possible vibrational signature of dissociation. Multi-reference CASSCF calculations are used to check the assumptions of the density functional approximations that were used to obtain the majority of the results

A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics

We survey the current state of millimeterwave (mmWave) radar applications in robotics with a focus on unique capabilities, and discuss future opportunities based on the state of the art. Frequency Modulated Continuous Wave (FMCW) mmWave radars operating in the 76--81GHz range are an appealing alternative to lidars, cameras and other sensors operating in the near visual spectrum. Radar has been made more widely available in new packaging classes, more convenient for robotics and its longer wavelengths have the ability to bypass visual clutter such as fog, dust, and smoke. We begin by covering radar principles as they relate to robotics. We then review the relevant new research across a broad spectrum of robotics applications beginning with motion estimation, localization, and mapping. We then cover object detection and classification, and then close with an analysis of current datasets and calibration techniques that provide entry points into radar research.Comment: 19 Pages, 11 Figures, 2 Tables, TRO Submission pendin

Foods contributing to nutrients intake and assessment of nutritional status in pre-dialysis patients: a cross-sectional study

Abstract Background For chronic kidney disease (CKD) patients, management of nutritional status is critical for delaying progression to end-stage renal disease. The purpose of this study is to provide the basis for personalized nutritional intervention in pre-dialysis patients by comparing the foods contributing to nutrients intake, nutritional status and potential dietary inflammation of CKD patients according to the diabetes mellitus (DM) comorbidity and CKD stage. Methods Two hundred fifty-six outpatients referred to the Department of Nephrology at SNUH from Feb 2016 to Jan 2017 were included. Subjects on dialysis and those who had undergone kidney transplantation were excluded. Bioelectrical impedance analysis (BIA), subjective global assessment (SGA), dietary intake, and biochemical parameters were collected. Subjects were classified into 4 groups according to DM comorbidity (DM or Non-DM) and CKD stage (Early or Late) by kidney function. Two-way analysis of variance and multinomial logistic regression analysis were performed for statistical analysis. Results Total number of malnourished patients was 31 (12.1%), and all of them were moderately malnourished according to SGA. The body mass index (BMI) of the DM-CKD group was significantly higher than the Non-DM-CKD group. The contribution of whole grains and legumes to protein intake in the DM-CKD group was greater than that in the Non-DM-CKD group. The DM- Early-CKD group consumed more whole grains and legumes compared with the Non-DM-Early-CKD group. The subjects in the lowest tertile for protein intake had lower phase angle, SGA score and serum albumin levels than those in the highest tertile. The potential for diet-induced inflammation did not differ among the groups. Conclusions Significant differences in intakes of whole grains and legumes between CKD patients with or without DM were observed. Since contribution of whole grains and legumes to phosphorus and potassium intake were significant, advice regarding whole grains and legumes may be needed in DM-CKD patients if phosphorus and potassium intake levels should be controlled. The nutritional status determined by BIA, SGA and serum albumin was found to be different depending on the protein intake. Understanding the characteristics of food sources can provide a basis for individualized nutritional intervention for CKD patients depending on the presence of diabetes

Quantum Chemical Studies of Four-center Iron CO2 Reduction Electrocatalyst and Multifaceted Development of a Small Molecule Force Field

The development of carbon dioxide (CO2) reduction electrocatalysts is an intensively studied area in the development of CO2 capture, utilization, and storage strategies. In this work, density functional theory (DFT) and ab initio molecular dynamics (AIMD) are employed to study redox properties and the pathway of a side reaction of CO2 reduction electrocatalyst [Fe4N(CO)12]-. The material of this chapter was published as an article titled “Quantum chemical studies of redox properties and conformational changes of a four-center iron CO2 reduction electrocatalyst” in Chemical Science (2018). I also present the multifaceted development of a molecular mechanics force field, an important piece of molecular mechanics simulations that is widely used for molecular structure and property prediction. An open-source Python package for restrained electrostatic potential (RESP), respyte is implemented for developing improved electrostatic potentials (ESP) based charge models for a better description of electrostatic interactions in force fields. I describe further improvements in Open Force Field small molecule force field after the first optimized small molecule force field from Open Force Field Initiative, which includes modification of parameter set for improved performance in certain chemical spaces, and a more careful design of quantum mechanics (QM) training data used to re-optimize bonded parameters

Quantum Chemical Studies of Four-center Iron CO2 Reduction Electrocatalyst and Multifaceted Development of a Small Molecule Force Field

The development of carbon dioxide (CO2) reduction electrocatalysts is an intensively studied area in the development of CO2 capture, utilization, and storage strategies. In this work, density functional theory (DFT) and ab initio molecular dynamics (AIMD) are employed to study redox properties and the pathway of a side reaction of CO2 reduction electrocatalyst [Fe4N(CO)12]-. The material of this chapter was published as an article titled “Quantum chemical studies of redox properties and conformational changes of a four-center iron CO2 reduction electrocatalyst” in Chemical Science (2018). I also present the multifaceted development of a molecular mechanics force field, an important piece of molecular mechanics simulations that is widely used for molecular structure and property prediction. An open-source Python package for restrained electrostatic potential (RESP), respyte is implemented for developing improved electrostatic potentials (ESP) based charge models for a better description of electrostatic interactions in force fields. I describe further improvements in Open Force Field small molecule force field after the first optimized small molecule force field from Open Force Field Initiative, which includes modification of parameter set for improved performance in certain chemical spaces, and a more careful design of quantum mechanics (QM) training data used to re-optimize bonded parameters

Analysis on Effective UAS Survey Conditions for Classification of Coastal Sediments

This study aims to introduce effective unmanned aerial system (UAS) survey conditions for coastal sediment classification, including muddy sand, sand, gravel, and shells in a tidal flat area. UAS images with resolutions ranging from 2 to 60 mm are used as an implication of survey altitudes. The UAS images are used for sediment classification using random forest (RF) and support vector machine (SVM) methods. The results showed that RF is more effective in sediment classification while the general accuracy pattern was similar. The accuracy decreased with lower spatial resolutions. Notably, there is a significant drop of accuracy with a resolution coarser than 40 mm. Considering the training data selection, classification accuracy, and survey efficiency, it is suggested that 40 mm UAS images would provide optimal condition with acceptable accuracy for coastal sediment classification using RF model. To gain higher accuracy, a lower flight altitude is required, which will elongate the survey time significantly. Given the fact that this study is the first approach to test various UAS survey conditions for coastal sediment classifications in a field condition; the methodology and findings of this study can serve as a guideline framework for future coastal UAS sediment mapping

Analysis on Effective UAS Survey Conditions for Classification of Coastal Sediments

This study aims to introduce effective unmanned aerial system (UAS) survey conditions for coastal sediment classification, including muddy sand, sand, gravel, and shells in a tidal flat area. UAS images with resolutions ranging from 2 to 60 mm are used as an implication of survey altitudes. The UAS images are used for sediment classification using random forest (RF) and support vector machine (SVM) methods. The results showed that RF is more effective in sediment classification while the general accuracy pattern was similar. The accuracy decreased with lower spatial resolutions. Notably, there is a significant drop of accuracy with a resolution coarser than 40 mm. Considering the training data selection, classification accuracy, and survey efficiency, it is suggested that 40 mm UAS images would provide optimal condition with acceptable accuracy for coastal sediment classification using RF model. To gain higher accuracy, a lower flight altitude is required, which will elongate the survey time significantly. Given the fact that this study is the first approach to test various UAS survey conditions for coastal sediment classifications in a field condition; the methodology and findings of this study can serve as a guideline framework for future coastal UAS sediment mapping

Data augmentation using image translation for underwater sonar image segmentation

Copyright: © 2022 Lee et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.In underwater environment, the study of object recognition is an important basis for implementing an underwater unmanned vessel. For this purpose, abundant experimental data to train deep learning model is required. However, it is very difficult to obtain these data because the underwater experiment itself is very limited in terms of preparation time and resources. In this study, the image transformation model, Pix2Pix is utilized to generate data similar to experimental one obtained by our ROV named SPARUS between the pool and reservoir. These generated data are applied to train the other deep learning model, FCN for a pixel segmentation of images. The original sonar image and its mask image have to be prepared for all training data to train the image segmentation model and it takes a lot of effort to do it what if all training data are supposed to be real sonar images. Fortunately, this burden can be released here, for the pairs of mask image and synthesized sonar image are already consisted in the image transformation step. The validity of the proposed procedures is verified from the performance of the image segmentation result. In this study, when only real sonar images are used for training, the mean accuracy is 0.7525 and the mean IoU is 0.7275. When the both synthetic and real data is used for training, the mean accuracy is 0.81 and the mean IoU is 0.7225. Comparing the results, the performance of mean accuracy increase to 6%, performance of the mean IoU is similar value.N