Siamese-DETR for Generic Multi-Object Tracking

The ability to detect and track the dynamic objects in different scenes is fundamental to real-world applications, e.g., autonomous driving and robot navigation. However, traditional Multi-Object Tracking (MOT) is limited to tracking objects belonging to the pre-defined closed-set categories. Recently, Open-Vocabulary MOT (OVMOT) and Generic MOT (GMOT) are proposed to track interested objects beyond pre-defined categories with the given text prompt and template image. However, the expensive well pre-trained (vision-)language model and fine-grained category annotations are required to train OVMOT models. In this paper, we focus on GMOT and propose a simple but effective method, Siamese-DETR, for GMOT. Only the commonly used detection datasets (e.g., COCO) are required for training. Different from existing GMOT methods, which train a Single Object Tracking (SOT) based detector to detect interested objects and then apply a data association based MOT tracker to get the trajectories, we leverage the inherent object queries in DETR variants. Specifically: 1) The multi-scale object queries are designed based on the given template image, which are effective for detecting different scales of objects with the same category as the template image; 2) A dynamic matching training strategy is introduced to train Siamese-DETR on commonly used detection datasets, which takes full advantage of provided annotations; 3) The online tracking pipeline is simplified through a tracking-by-query manner by incorporating the tracked boxes in previous frame as additional query boxes. The complex data association is replaced with the much simpler Non-Maximum Suppression (NMS). Extensive experimental results show that Siamese-DETR surpasses existing MOT methods on GMOT-40 dataset by a large margin

Effect of mixing on mass transfer characterization in continuous slugs and dispersed droplets in biphasic slug flow microreactors

The mass transfer of slug flow, a widely applied flow pattern in microreactors, is still difficult to predict mainly due to the competing nature between convection and diffusion. This work focused on the influence of the mixing mechanism on the mass transfer performance under gas–liquid and liquid–liquid slug flow, in both continuous slugs and dispersed droplets. Colorimetric study with the resazurin oxidation system was implemented, where the mass transfer resistance was constantly located in the aqueous phase. In the hydrophilic glass microreactor, the convection featured by intensive internal circulation and/or inter-slug leakage flow dominated diffusion, leading to nearly-constant kLa along the channel under given flow rates. However, in the hydrophobic PTFE capillary, the stagnant region constituted a significant share in the aqueous droplet, indicating the prominent role of diffusion against convection therein. As a result, kLa values decreased along the main channel length in fixed operating conditions. Accordingly, prediction models were respectively correlated depending on mixing mechanisms. Moreover, mass transfer contributions from the bubble and droplet formation stages were also investigated. This work is expected to shed light on judicious process design and reliable predictions in microreactor operations

Global μ

The complex-valued neural networks with unbounded time-varying delays are considered. By constructing appropriate Lyapunov-Krasovskii functionals, and employing the free weighting matrix method, several delay-dependent criteria for checking the global μ-stability of the addressed complex-valued neural networks are established in linear matrix inequality (LMI), which can be checked numerically using the effective LMI toolbox in MATLAB. Two examples with simulations are given to show the effectiveness and less conservatism of the proposed criteria

Stability analysis of impulsive stochastic Cohen–Grossberg neural networks with mixed time delays

This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdIn this paper, the problem of stability analysis for a class of impulsive stochastic Cohen–Grossberg neural networks with mixed delays is considered. The mixed time delays comprise both the time-varying and infinite distributed delays. By employing a combination of the M-matrix theory and stochastic analysis technique, a sufficient condition is obtained to ensure the existence, uniqueness, and exponential p-stability of the equilibrium point for the addressed impulsive stochastic Cohen–Grossberg neural network with mixed delays. The proposed method, which does not make use of the Lyapunov functional, is shown to be simple yet effective for analyzing the stability of impulsive or stochastic neural networks with variable and/or distributed delays. We then extend our main results to the case where the parameters contain interval uncertainties. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. An example is given to show the effectiveness of the obtained results.This work was supported by the Natural Science Foundation of CQ CSTC under grant 2007BB0430, the Scientific Research Fund of Chongqing Municipal Education Commission under Grant KJ070401, an International Joint Project sponsored by the Royal Society of the UK and the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany

Circular Optical Phased Array with Large Steering Range and High Resolution

Light detection and ranging systems based on optical phased arrays and integrated silicon photonics have sparked a surge of applications over the recent years. This includes applications in sensing, free-space communications, or autonomous vehicles, to name a few. Herein, we report a design of two-dimensional optical phased arrays, which are arranged in a grid of concentric rings. We numerically investigate two designs composed of 110 and 820 elements, respectively. Both single-wavelength (1550 nm) and broadband multi-wavelength (1535 nm to 1565 nm) operations are studied. The proposed phased arrays enable free-space beam steering, offering improved performance with narrow beam divergences of only 0.5° and 0.22° for the 110-element and 820-element arrays, respectively, with a main-to-sidelobe suppression ratio higher than 10 dB. The circular array topology also allows large element spacing far beyond the sub-wavelength-scaled limits that are present in one-dimensional linear or two-dimensional rectangular arrays. Under a single-wavelength operation, a solid-angle steering between 0.21π sr and 0.51π sr is obtained for 110- and 820-element arrays, respectively, while the beam steering spans the range of 0.24π sr and 0.57π sr for a multi-wavelength operation. This work opens new opportunities for future optical phased arrays in on-chip photonic applications, in which fast, high-resolution, and broadband beam steering is necessary.This work was supported by the Natural Sciences and Engineering Research Council of Canada’s Collaborative R&D Grant Program by collaborating with Optiwave Systems, Inc., Slovak Grant Agency VEGA 1/0113/22, and Slovak Research and Development Agency under the project APVV-21-0217. Partial funding for open access charge: Universidad de Málag