Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency

Visual simultaneous localization and mapping (SLAM) systems face challenges in detecting loop closure under the circumstance of large viewpoint changes. In this paper, we present an object-based loop closure detection method based on the spatial layout and semanic consistency of the 3D scene graph. Firstly, we propose an object-level data association approach based on the semantic information from semantic labels, intersection over union (IoU), object color, and object embedding. Subsequently, multi-view bundle adjustment with the associated objects is utilized to jointly optimize the poses of objects and cameras. We represent the refined objects as a 3D spatial graph with semantics and topology. Then, we propose a graph matching approach to select correspondence objects based on the structure layout and semantic property similarity of vertices' neighbors. Finally, we jointly optimize camera trajectories and object poses in an object-level pose graph optimization, which results in a globally consistent map. Experimental results demonstrate that our proposed data association approach can construct more accurate 3D semantic maps, and our loop closure method is more robust than point-based and object-based methods in circumstances with large viewpoint changes

Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schr\"odinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction tool with reduced complexity and good generalization capability. However, the complexity of long grid input points and the flexibility of neural network structure should be further optimized for broader applications. Here, we propose a convolutional feature separation modeling method to predict full-field ultrafast nonlinear dynamics with low complexity and high flexibility, where the linear effects are firstly modeled by NLSE-derived methods, then a convolutional deep learning method is implemented for nonlinearity modeling. With this method, the temporal relevance of nonlinear effects is substantially shortened, and the parameters and scale of neural networks can be greatly reduced. The running time achieves a 94% reduction versus NLSE and an 87% reduction versus RNN without accuracy deterioration. In addition, the input pulse conditions, including grid point numbers, durations, peak powers, and propagation distance, can be flexibly changed during the predicting process. The results represent a remarkable improvement in the ultrafast nonlinear dynamics prediction and this work also provides novel perspectives of the feature separation modeling method for quickly and flexibly studying the nonlinear characteristics in other fields.Comment: 15 pages,9 figure

Case report: Unveiling the unforeseen: a catastrophic encounter of giant aortic aneurysm rupture during re-sternotomy in a patient with bicuspid aortic valve and previous surgical aortic valve replacement

Due to structural abnormalities in the leaflets, patients with bicuspid aortic valve (BAV) may develop isolated aortic valve disease, such as aortic regurgitation, aortic stenosis, or a combination of both. In addition to valvular pathology, numerous studies have indicated that approximately 40% of BAV patients exhibit aortic pathologies characterized by aortic dilatation. According to guidelines for valvular diseases, patients with BAV who require surgical aortic valve replacement (SAVR) and have a diameter of the aortic sinuses or ascending aorta ≥4.5 cm are recommended to undergo concomitant replacement of the aortic sinuses or ascending aorta. However, we encountered a case in 2020 involving a patient with severe aortic regurgitation due to BAV and an ascending aortic diameter of 4.2 cm. This patient underwent SAVR and ascending aortoplasty surgery at our center. Remarkably, three years postoperatively, the patient's aortic diameter rapidly expanded by nearly threefold, which also suggests the risk of encountering a giant aortic root aneurysm during reoperation. Unfortunately, a fatal rupture of a giant aortic root aneurysm was encountered during re-sternotomy. Fortunately, with adequate preoperative planning, we successfully managed to avert this perilous situation. The patient recovered without complications and was discharged on the 8th day. Individualized surgical plans were formulated based on a comprehensive evaluation of the perioperative conditions

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Fast and accurate waveform modeling of long-haul multi-channel optical fiber transmission using a hybrid model-data driven scheme

The modeling of optical wave propagation in optical fiber is a task of fast and accurate solving the nonlinear Schr\"odinger equation (NLSE), and can enable the optical system design, digital signal processing verification and fast waveform calculation. Traditional waveform modeling of full-time and full-frequency information is the split-step Fourier method (SSFM), which has long been regarded as challenging in long-haul wavelength division multiplexing (WDM) optical fiber communication systems because it is extremely time-consuming. Here we propose a linear-nonlinear feature decoupling distributed (FDD) waveform modeling scheme to model long-haul WDM fiber channel, where the channel linear effects are modelled by the NLSE-derived model-driven methods and the nonlinear effects are modelled by the data-driven deep learning methods. Meanwhile, the proposed scheme only focuses on one-span fiber distance fitting, and then recursively transmits the model to achieve the required transmission distance. The proposed modeling scheme is demonstrated to have high accuracy, high computing speeds, and robust generalization abilities for different optical launch powers, modulation formats, channel numbers and transmission distances. The total running time of FDD waveform modeling scheme for 41-channel 1040-km fiber transmission is only 3 minutes versus more than 2 hours using SSFM for each input condition, which achieves a 98% reduction in computing time. Considering the multi-round optimization by adjusting system parameters, the complexity reduction is significant. The results represent a remarkable improvement in nonlinear fiber modeling and open up novel perspectives for solution of NLSE-like partial differential equations and optical fiber physics problems.Comment: 8 pages, 5 figures, 1 table, 30 reference

Flexible Capacitive Pressure Sensor with High Sensitivity and Wide Range Based on a Cheetah Leg Structure via 3D Printing

Flexible pressure sensors can be used in human–computer interaction and wearable electronic devices, but one main challenge is to fabricate capacitive sensors with a wide pressure range and high sensitivity. Here, we designed a capacitive pressure sensor based on a bionic cheetah leg microstructure, validated the benefits of the bionic microstructure design, and optimized the structural feature parameters using 3D printing technology. The pressure sensor inspired by the cheetah leg shape has a high sensitivity (0.75 kPa–1), a wide linear sensing range (0–280 kPa), a fast response time of roughly 80 ms, and outstanding durability (24,000 cycles). Furthermore, the sensor can recognize a finger-operated mouse, monitor human motion, and transmit Morse code information. This work demonstrates that bionic capacitive pressure sensors hold considerable promise for use in wearable devices

Flexible Capacitive Pressure Sensor with High Sensitivity and Wide Range Based on a Cheetah Leg Structure via 3D Printing

Flexible pressure sensors can be used in human–computer interaction and wearable electronic devices, but one main challenge is to fabricate capacitive sensors with a wide pressure range and high sensitivity. Here, we designed a capacitive pressure sensor based on a bionic cheetah leg microstructure, validated the benefits of the bionic microstructure design, and optimized the structural feature parameters using 3D printing technology. The pressure sensor inspired by the cheetah leg shape has a high sensitivity (0.75 kPa–1), a wide linear sensing range (0–280 kPa), a fast response time of roughly 80 ms, and outstanding durability (24,000 cycles). Furthermore, the sensor can recognize a finger-operated mouse, monitor human motion, and transmit Morse code information. This work demonstrates that bionic capacitive pressure sensors hold considerable promise for use in wearable devices

Flexible Capacitive Pressure Sensor with High Sensitivity and Wide Range Based on a Cheetah Leg Structure via 3D Printing

Flexible pressure sensors can be used in human–computer interaction and wearable electronic devices, but one main challenge is to fabricate capacitive sensors with a wide pressure range and high sensitivity. Here, we designed a capacitive pressure sensor based on a bionic cheetah leg microstructure, validated the benefits of the bionic microstructure design, and optimized the structural feature parameters using 3D printing technology. The pressure sensor inspired by the cheetah leg shape has a high sensitivity (0.75 kPa–1), a wide linear sensing range (0–280 kPa), a fast response time of roughly 80 ms, and outstanding durability (24,000 cycles). Furthermore, the sensor can recognize a finger-operated mouse, monitor human motion, and transmit Morse code information. This work demonstrates that bionic capacitive pressure sensors hold considerable promise for use in wearable devices

Flexible Capacitive Pressure Sensor with High Sensitivity and Wide Range Based on a Cheetah Leg Structure via 3D Printing

Flexible pressure sensors can be used in human–computer interaction and wearable electronic devices, but one main challenge is to fabricate capacitive sensors with a wide pressure range and high sensitivity. Here, we designed a capacitive pressure sensor based on a bionic cheetah leg microstructure, validated the benefits of the bionic microstructure design, and optimized the structural feature parameters using 3D printing technology. The pressure sensor inspired by the cheetah leg shape has a high sensitivity (0.75 kPa–1), a wide linear sensing range (0–280 kPa), a fast response time of roughly 80 ms, and outstanding durability (24,000 cycles). Furthermore, the sensor can recognize a finger-operated mouse, monitor human motion, and transmit Morse code information. This work demonstrates that bionic capacitive pressure sensors hold considerable promise for use in wearable devices

A chemically mediated artificial neuron

Brain–machine interfaces typically rely on electrophysiological signals to interpret and transmit neurological information. In biological systems, however, neurotransmitters are chemical-based interneuron messengers. This mismatch can potentially lead to incorrect interpretation of the transmitted neuron information. Here we report a chemically mediated artificial neuron that can receive and release the neurotransmitter dopamine. The artificial neuron detects dopamine using a carbon-based electrochemical sensor and then processes the sensory signals using a memristor with synaptic plasticity, before stimulating dopamine release through a heat-responsive hydrogel. The system responds to dopamine exocytosis from rat pheochromocytoma cells and also releases dopamine to activate pheochromocytoma cells, forming a chemical communication loop similar to interneurons. To illustrate the potential of this approach, we show that the artificial neuron can trigger the controllable movement of a mouse leg and robotic hand.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)National Research Foundation (NRF)Submitted/Accepted versionWe acknowledge financial support from the National Key Research and Development Program of China (2017YFA0205302, L.W.); Natural Science Foundation of Jiangsu Province—Major Project (BK20212012, L.W.); National Key R&D Program of China (2021YFB3601200, M.W.); National Natural Science Foundation of China (81971701, B.H.); the Natural Science Foundation for Young Scholars of Jiangsu Province (BK20210596, T.W.); the Natural Science Foundation of Jiangsu Province (BK20201352, B.H.); the Program of Jiangsu Specially-Appointed Professor (B.H. and T.W.); Science Foundation of Nanjing University of Post and Telecommunications (NUPTSF, NY221004, T.W.); the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project #A18A1b0045, X.C.); the National Research Foundation (NRF), Prime Minister’s Office, Singapore, under its NRF Investigatorship (NRF-NRFI2017-07, X.C.); and Singapore Ministry of Education (MOE2017-T2-2-107, X.C.)