Deep Forest-Based Monocular Visual Sign Language Recognition

Sign language recognition (SLR) is a bridge linking the hearing impaired and the general public. Some SLR methods using wearable data gloves are not portable enough to provide daily sign language translation service, while visual SLR is more flexible to work with in most scenes. This paper introduces a monocular vision-based approach to SLR. Human skeleton action recognition is proposed to express semantic information, including the representation of signs’ gestures, using the regularization of body joint features and a deep-forest-based semantic classifier with a voting strategy. We test our approach on the public American Sign Language Lexicon Video Dataset (ASLLVD) and a private testing set. It proves to achieve a promising performance and shows a high generalization capability on the testing set

Recursive Least Squares Based Refinement Network for Vehicle Trajectory Prediction

Trajectory prediction of surrounding objects plays a pivotal role in the field of autonomous driving vehicles. In the current rollout process, it suffers from an accumulation of errors, which has a negative impact on prediction accuracy. This paper proposes a parametric-learning recursive least-squares (RLS) method integrated with an encoder–decoder framework for trajectory prediction, named the recursive least-squares-based refinement network (RRN). Through the generation of several anchors in the future trajectory, RRN can capture both local and global motion patterns. We conducted experiments on the prevalent NGSIM and INTERACTION datasets, which contain various scenarios such as highways, intersections and roundabouts. The promising results indicate that RRN could improve the performance of the rollout trajectory prediction effectively

Recursive Least Squares Based Refinement Network for Vehicle Trajectory Prediction

Trajectory prediction of surrounding objects plays a pivotal role in the field of autonomous driving vehicles. In the current rollout process, it suffers from an accumulation of errors, which has a negative impact on prediction accuracy. This paper proposes a parametric-learning recursive least-squares (RLS) method integrated with an encoder–decoder framework for trajectory prediction, named the recursive least-squares-based refinement network (RRN). Through the generation of several anchors in the future trajectory, RRN can capture both local and global motion patterns. We conducted experiments on the prevalent NGSIM and INTERACTION datasets, which contain various scenarios such as highways, intersections and roundabouts. The promising results indicate that RRN could improve the performance of the rollout trajectory prediction effectively

Inorganic Halide Perovskite Solar Cells: Progress and Challenges

All-inorganic perovskite semiconductors have recently drawn increasing attention owing to their outstanding thermal stability. Although all-inorganic perovskite solar cells (PSCs) have achieved significant progress in recent years, they still fall behind their prototype organic–inorganic counterparts owing to severe energy losses. Therefore, there is considerable interest in further improving the performance of all-inorganic PSCs by synergic optimi-zation of perovskite films and device interfaces. This review article provides an overview of recent progress in inorganic PSCs in terms of lead-based and lead-free composition. The physical properties of all-inorganic perovskite semiconductors as well as the hole/electron transporting materials are dis-cussed to unveil the important role of composition engineering and interface modification. Finally, a discussion of the prospects and challenges for all-inorganic PSCs in the near future is presente

Composition Engineering of All‐Inorganic Perovskite Film for Efficient and Operationally Stable Solar Cells

Cesium‐based inorganic perovskites have recently attracted great research focus due to their excellent optoelectronic properties and thermal stability. However, the operational instability of all‐inorganic perovskites is still a main hindrance for the commercialization. Herein, a facile approach is reported to simultaneously enhance both the efficiency and long‐term stability for all‐inorganic CsPbI2.5Br0.5 perovskite solar cells via inducing excess lead iodide (PbI2) into the precursors. Comprehensive film and device characterizations are conducted to study the influences of excess PbI2 on the crystal quality, passivation effect, charge dynamics, and photovoltaic performance. It is found that excess PbI2 improves the crystallization process, producing high‐quality CsPbI2.5Br0.5 films with enlarged grain sizes, enhanced crystal orientation, and unchanged phase composition. The residual PbI2 at the grain boundaries also provides a passivation effect, which improves the optoelectronic properties and charge collection property in optimized devices, leading to a power conversion efficiency up to 17.1% with a high open‐circuit voltage of 1.25 V. More importantly, a remarkable long‐term operational stability is also achieved for the optimized CsPbI2.5Br0.5 solar cells, with less than 24% degradation drop at the maximum power point under continuous illumination for 420 h

Spacer Engineering of Diammonium‐Based 2D Perovskites toward Efficient and Stable 2D/3D Heterostructure Perovskite Solar Cells

Perovskite solar cells (PSCs) based on 2D/3D heterostructures show great potential to combine the advantages of the high efficiency of 3D perovskites and the high stability of 2D perovskites. However, an in-depth understanding of the organic-spacer effects on the 2D quantum well (QW) structures and electronic properties at the 2D/3D interfaces is yet to be fully achieved, especially in the case of 2D perovskites based on diammonium spacers/ligands. Here, a series of diammonium spacers is considered for the construct ion 2D/3D perovskite heterostructures. It is found that the chemical structure and concentration of the spacers can dramatically affect the characteristics of the 2D capping layers, including their phase purity and orientation. Density functional theory calculations indicate that the spacer modifications can induce shifts in the energy-level alignments at the 2D/3D interfaces and therefore influence the charge-transfer characteristics. The strong intermolecular interactions between the 2,2-(ethylenedioxy)bis(ethylammonium) (EDBE) cations and inorganic [PbI6]4− slabs facilitate a controlled deposition of a phase-pure QW structure (n = 1) with a horizontal orientation, which leads to better surface passivation and carrier extraction. These benefits endow the EDBE-based 2D/3D devices with a high power conversion efficiency of 22.6% and remarkable environmental stability, highlighting the promise of spacer-chemistry design for high-performance 2D/3D PSCs.National Natural Science Foundation of China12 month embargo; first published: 31 October 2021This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]