Evaluation of Temporal Spacing Errors Associated with Interval Management Algorithms

This paper seeks to characterize the temporal spacing errors resulting from the use of Interval Management (IM) algorithms. The focus of the current paper is IM concepts and algorithms that realize a specified temporal spacing between a Target aircraft and an Ownship aircraft at the runway threshold. The paper presents an IM algorithm consisting of the following four modules: (i) Target-Landing-Time Estimation Module, (ii) Ownship-Landing-Time Estimation Module, (iii) Ownship Speed Command Computation Module, and (iv) Ownship Thrust Command Computation Module. The overall guidance module is evaluated on a simulation that models aircraft point-mass dynamics, bank-angle auto-pilot dynamics, pitch-axis auto-pilot dynamics, and engine lag dynamics. The simulation environment also consists of actual atmospheric forecasts and realistic spatio-temporally correlated wind uncertainty models. Results obtained from single case simulation as well as Monte-Carlo simulations are presented in the paper. The modeled scenario consisted of an A320 Target equipped with Lateral Navigation/Vertical Navigation (LNAV/VNAV) capabilities followed by an A320 Ownship equipped with the IM algorithm. Both aircraft fly the BIGSUR route to SFO airport using a RAP-13 1-hr wind forecast. 500 Monte-Carlo simulations were conducted with realistic wind uncertainty models. The IM algorithm for this case is seen to have a 90% probability landing time error range of 5.9 seconds, compared to the no-IM solution, which has a 90% probability landing time error range of 33.4 seconds

RTN: Reparameterized Ternary Network

To deploy deep neural networks on resource-limited devices, quantization has been widely explored. In this work, we study the extremely low-bit networks which have tremendous speed-up, memory saving with quantized activation and weights. We first bring up three omitted issues in extremely low-bit networks: the squashing range of quantized values; the gradient vanishing during backpropagation and the unexploited hardware acceleration of ternary networks. By reparameterizing quantized activation and weights vector with full precision scale and offset for fixed ternary vector, we decouple the range and magnitude from the direction to extenuate the three issues. Learnable scale and offset can automatically adjust the range of quantized values and sparsity without gradient vanishing. A novel encoding and computation pat-tern are designed to support efficient computing for our reparameterized ternary network (RTN). Experiments on ResNet-18 for ImageNet demonstrate that the proposed RTN finds a much better efficiency between bitwidth and accuracy, and achieves up to 26.76% relative accuracy improvement compared with state-of-the-art methods. Moreover, we validate the proposed computation pattern on Field Programmable Gate Arrays (FPGA), and it brings 46.46x and 89.17x savings on power and area respectively compared with the full precision convolution.Comment: To appear at AAAI-2

Improving Highway Work Zone Safety

Highway work zones disrupt normal traffic flow and can create severe safety problems. Due to the rising needs in highway maintenance and construction in the United States, the number of work zones is increasing nationwide. With a total of 1,010 fatalities and more than 40,000 injuries occurring in 2006, improvements in work zone safety are necessary. The three primary objectives of this research project included: 1) to determine the effectiveness of a Portable Changeable Message Sign (PCMS) in reducing vehicle speeds on two-lane, rural highway work zones; 2) to determine the effectiveness of a Temporary Traffic Sign (TTS), (W20-1, “Road Work Ahead”); and 3) to determine motorists’ responses to the signage. To accomplish these objectives, field experiments were conducted at US-36 and US-73 in Seneca and Hiawatha, Kansas, respectively. During the field experiments, an evaluation of the effectiveness of the PCMS was conducted under three different conditions: 1) PCMS on; 2) PCMS off, but still visible; and 3) PCMS removed from the road and out of sight. The researchers also divided the vehicles into three classes (passenger car, truck, and semitrailer) and compared the mean speed change of these classes based on three different sign setups: PCMS on, PCMS off, and the use of the TTS (W20-1, “Road Work Ahead”). A survey was also conducted at the experimental work zones to obtain a general understanding of the motorists’ attitudes as they traveled through the construction areas. Based on the data analysis results, researchers concluded that the presence of the PCMS effectively reduced vehicle speeds on two-lane highway work zones. A slow speed is more likely to reduce the probability of a crash or the severity of a crash. In addition, researchers performed a univariate analysis of the variance test to determine if a significant interaction existed between motorists’ responses and the sign conditions. The results showed a significant interaction between the signs and passenger car vehicles

Spectral response measurements of Perovskite solar cells

A new spectral response measurement routine is proposed that is universally applicable for all perovskite devices. It is aimed at improving measurement accuracy and repeatability of spectral response curves and current-voltage curve spectral mismatch factor corrections. Frequency response, effects of preconditioning as well as dependency on incident light intensity and voltage load on spectral response measurements are characterized on two differently structured perovskite device types. It is shown that device preconditioning affects the spectral response shape, causing errors in spectral mismatch factor corrections of up to 0.8% when using a reference cell with a good spectral match and a class A solar simulator. Wavelength dependent response to incident light intensity and voltage load is observed on both device types, which highlights the need to measure at short circuit current and maximum power point to correct spectral mismatch. The method with recommendations given ensures the correct measurement conditions are applied and measurements are corrected for instability in performance

The Structural, Electronic, and Optical Properties of Ge/Si Quantum Wells: Lasing at a Wavelength of 1550 nm

The realization of a fully integrated group IV electrically driven laser at room temperature is an essential issue to be solved. We introduced a novel group IV side-emitting laser at a wavelength of 1550 nm based on a 3-layer Ge/Si quantum well (QW). By designing this scheme, we showed that the structural, electronic, and optical properties are excited for lasing at 1550 nm. The preliminary results show that the device can produce a good light spot shape convenient for direct coupling with the waveguide and single-mode light emission. The laser luminous power can reach up to 2.32 mW at a wavelength of 1550 nm with a 300-mA current. Moreover, at room temperature (300 K), the laser can maintain maximum light power and an ideal wavelength (1550 nm). Thus, this study provides a novel approach to reliable, efficient electrically pumped silicon-based lasers

Spectral Response Measurements of Perovskite Solar Cells

A new spectral response (SR) measurement routine is proposed that is universally applicable for all perovskite devices. It is aimed at improving measurement accuracy and repeatability of SR curves and current–voltage curve spectral mismatch factor (MMF) corrections. Frequency response, effects of preconditioning as well as dependency on incident light intensity and voltage load on SR measurements are characterized on two differently structured perovskite device types. It is shown that device preconditioning affects the SR shape, causing errors in spectral MMF corrections of up to 0.8% when using a reference cell with a good spectral match and a class A solar simulator. Wavelength dependent response to incident light intensity and voltage load is observed on both device types, which highlights the need to measure at short-circuit current and maximum power point to correct spectral mismatch. The method with recommendations given ensures that the correct measurement conditions are applied and measurements are corrected for instability in performance

Mixed halide perovskites for spectrally stable and high-efficiency blue light-emitting diodes.

Bright and efficient blue emission is key to further development of metal halide perovskite light-emitting diodes. Although modifying bromide/chloride composition is straightforward to achieve blue emission, practical implementation of this strategy has been challenging due to poor colour stability and severe photoluminescence quenching. Both detrimental effects become increasingly prominent in perovskites with the high chloride content needed to produce blue emission. Here, we solve these critical challenges in mixed halide perovskites and demonstrate spectrally stable blue perovskite light-emitting diodes over a wide range of emission wavelengths from 490 to 451 nanometres. The emission colour is directly tuned by modifying the halide composition. Particularly, our blue and deep-blue light-emitting diodes based on three-dimensional perovskites show high EQE values of 11.0% and 5.5% with emission peaks at 477 and 467 nm, respectively. These achievements are enabled by a vapour-assisted crystallization technique, which largely mitigates local compositional heterogeneity and ion migration

CNN-based automatic segmentations and radiomics feature reliability on contrast-enhanced ultrasound images for renal tumors

ObjectiveTo investigate the feasibility and efficiency of automatic segmentation of contrast-enhanced ultrasound (CEUS) images in renal tumors by convolutional neural network (CNN) based models and their further application in radiomic analysis.Materials and methodsFrom 94 pathologically confirmed renal tumor cases, 3355 CEUS images were extracted and randomly divided into training set (3020 images) and test set (335 images). According to the histological subtypes of renal cell carcinoma, the test set was further split into clear cell renal cell carcinoma (ccRCC) set (225 images), renal angiomyolipoma (AML) set (77 images) and set of other subtypes (33 images). Manual segmentation was the gold standard and serves as ground truth. Seven CNN-based models including DeepLabV3+, UNet, UNet++, UNet3+, SegNet, MultilResUNet and Attention UNet were used for automatic segmentation. Python 3.7.0 and Pyradiomics package 3.0.1 were used for radiomic feature extraction. Performance of all approaches was evaluated by the metrics of mean intersection over union (mIOU), dice similarity coefficient (DSC), precision, and recall. Reliability and reproducibility of radiomics features were evaluated by the Pearson coefficient and the intraclass correlation coefficient (ICC).ResultsAll seven CNN-based models achieved good performance with the mIOU, DSC, precision and recall ranging between 81.97%-93.04%, 78.67%-92.70%, 93.92%-97.56%, and 85.29%-95.17%, respectively. The average Pearson coefficients ranged from 0.81 to 0.95, and the average ICCs ranged from 0.77 to 0.92. The UNet++ model showed the best performance with the mIOU, DSC, precision and recall of 93.04%, 92.70%, 97.43% and 95.17%, respectively. For ccRCC, AML and other subtypes, the reliability and reproducibility of radiomic analysis derived from automatically segmented CEUS images were excellent, with the average Pearson coefficients of 0.95, 0.96 and 0.96, and the average ICCs for different subtypes were 0.91, 0.93 and 0.94, respectively.ConclusionThis retrospective single-center study showed that the CNN-based models had good performance on automatic segmentation of CEUS images for renal tumors, especially the UNet++ model. The radiomics features extracted from automatically segmented CEUS images were feasible and reliable, and further validation by multi-center research is necessary