Ariel - Volume 3 Number 5

Editors Richard J. Bonanno Robin A. Edwards Associate Editors Steven Ager Tom Williams Lay-out Editor Eugenia Miller Contributing Editors Paul Bialas Robert Breckenridge Lynne Porter David Jacoby Terry Burt Mark Pearlman Michael Leo Mike LeWitt Editors Emeritus Delvyn C. Case., Jr. Paul M. Fernhof

Multiscale benchmarking of drug delivery vectors

Cross-system comparisons of drug delivery vectors are essential to ensure optimal design. An in-vitro experimental protocol is presented that separates the role of the delivery vector from that of its cargo in determining the cell response, thus allowing quantitative comparison of different systems. The technique is validated through benchmarking of the dose–response of human fibroblast cells exposed to the cationic molecule, polyethylene imine (PEI); delivered as a free molecule and as a cargo on the surface of CdSe nanoparticles and Silica microparticles. The exposure metrics are converted to a delivered dose with the transport properties of the different scale systems characterized by a delivery time, τ. The benchmarking highlights an agglomeration of the free PEI molecules into micron sized clusters and identifies the metric determining cell death as the total number of PEI molecules presented to cells, determined by the delivery vector dose and the surface density of the cargo

Pressure measurements in a low-density nozzle plume for code verification

Measurements of Pitot pressure were made in the exit plane and plume of a low-density, nitrogen nozzle flow. Two numerical computer codes were used to analyze the flow, including one based on continuum theory using the explicit MacCormack method, and the other on kinetic theory using the method of direct-simulation Monte Carlo (DSMC). The continuum analysis was carried to the nozzle exit plane and the results were compared to the measurements. The DSMC analysis was extended into the plume of the nozzle flow and the results were compared with measurements at the exit plane and axial stations 12, 24 and 36 mm into the near-field plume. Two experimental apparatus were used that differed in design and gave slightly different profiles of pressure measurements. The DSMC method compared well with the measurements from each apparatus at all axial stations and provided a more accurate prediction of the flow than the continuum method, verifying the validity of DSMC for such calculations

Analysis of the Influence of Cell Heterogeneity on Nanoparticle Dose Response

Understanding the effect of variability in the interaction of individual cells with nanoparticles on the overall response of the cell population to a nanoagent is a fundamental challenge in bionanotechnology. Here, we show that the technique of time-resolved, high-throughput microscopy can be used in this endeavor. Mass measurement with single-cell resolution provides statistically robust assessments of cell heterogeneity, while the addition of a temporal element allows assessment of separate processes leading to deconvolution of the effects of particle supply and biological response. We provide a specific demonstration of the approach, in vitro, through time-resolved measurement of fibroblast cell (HFF-1) death caused by exposure to cationic nanoparticles. The results show that heterogeneity in cell area is the major source of variability with area-dependent nanoparticle capture rates determining the time of cell death and hence the form of the exposure–response characteristic. Moreover, due to the particulate nature of the nanoparticle suspension, there is a reduction in the particle concentration over the course of the experiment, eventually causing saturation in the level of measured biological outcome. A generalized mathematical description of the system is proposed, based on a simple model of particle depletion from a finite supply reservoir. This captures the essential aspects of the nanoparticle–cell interaction dynamics and accurately predicts the population exposure–response curves from individual cell heterogeneity distributions

Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals

Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous cars. Key performance factors are a fine range resolution and the possibility to directly measure velocity. With a rising number of radar sensors and the so far unregulated automotive radar frequency band, mutual interference is inevitable and must be dealt with. Sensors must be capable of detecting, or even mitigating the harmful effects of interference, which include a decreased detection sensitivity. In this paper, we evaluate a Convolutional Neural Network (CNN)-based approach for interference mitigation on real-world radar measurements. We combine real measurements with simulated interference in order to create input-output data suitable for training the model. We analyze the performance to model complexity relation on simulated and measurement data, based on an extensive parameter search. Further, a finite sample size performance comparison shows the effectiveness of the model trained on either simulated or real data as well as for transfer learning. A comparative performance analysis with the state of the art emphasizes the potential of CNN-based models for interference mitigation and denoising of real-world measurements, also considering resource constraints of the hardware.Comment: 2020 IEEE International Radar Conference (RADAR

End-to-End Training of Neural Networks for Automotive Radar Interference Mitigation

In this paper we propose a new method for training neural networks (NNs) for frequency modulated continuous wave (FMCW) radar mutual interference mitigation. Instead of training NNs to regress from interfered to clean radar signals as in previous work, we train NNs directly on object detection maps. We do so by performing a continuous relaxation of the cell-averaging constant false alarm rate (CA-CFAR) peak detector, which is a well-established algorithm for object detection using radar. With this new training objective we are able to increase object detection performance by a large margin. Furthermore, we introduce separable convolution kernels to strongly reduce the number of parameters and computational complexity of convolutional NN architectures for radar applications. We validate our contributions with experiments on real-world measurement data and compare them against signal processing interference mitigation methods.Comment: 2023 IEEE International Radar Conference (RADAR), 6 pages, 4 figure

Angle-Equivariant Convolutional Neural Networks for Interference Mitigation in Automotive Radar

In automotive applications, frequency modulated continuous wave (FMCW) radar is an established technology to determine the distance, velocity and angle of objects in the vicinity of the vehicle. The quality of predictions might be seriously impaired if mutual interference between radar sensors occurs. Previous work processes data from the entire receiver array in parallel to increase interference mitigation quality using neural networks (NNs). However, these architectures do not generalize well across different angles of arrival (AoAs) of interferences and objects. In this paper we introduce fully convolutional neural network (CNN) with rank-three convolutions which is able to transfer learned patterns between different AoAs. Our proposed architecture outperforms previous work while having higher robustness and a lower number of trainable parameters. We evaluate our network on a diverse data set and demonstrate its angle equivariance.Comment: 4 pages, 3 figure