Effect and Evaluation of an Ultrasonic Atomizer With Large Vibration Amplitude

An ultrasonic atomizer can produce large vibration amplitude is designed. Different from the structure of the usually seen ultrasonic spray nozzle, the atomizer is fundamentally constructed with a hollow tube encircled with several pieces of sectional type piezoelectric actuators, which can radially oscillate the tube to generate desired vibration profile. Atomization is formed on the surface around the liquid outlet of the tube where maximum vibration amplitude occurs. In search of resonance frequency and vibration amplitude, modal and harmonic analyses of the ultrasonic atomizer are carried out by ANSYS. In comparison the simulated results with the experimental results, both are in good agreement. A measurement system is set up for detecting the atomization droplets and calculating the droplet size and distribution. An attempt is to design an ultrasonic atomizer can produce high distribution and small diameter droplets for some application-level requirements, droplet diameter around 20�60 lm is assumed to be the specification for performance verification of the proposed atomizer. In experiment, it is found nearly 90% of atomized droplets fit for the requirement. Besides the most important factor of operating frequency, a relation of amplitude is found to include in the well-known Lang and Rayleigh�s equation

Counting Crowds in Bad Weather

Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding. Numerous methods have been proposed and achieved state-of-the-art performance for real-world tasks. However, existing approaches do not perform well under adverse weather such as haze, rain, and snow since the visual appearances of crowds in such scenes are drastically different from those images in clear weather of typical datasets. In this paper, we propose a method for robust crowd counting in adverse weather scenarios. Instead of using a two-stage approach that involves image restoration and crowd counting modules, our model learns effective features and adaptive queries to account for large appearance variations. With these weather queries, the proposed model can learn the weather information according to the degradation of the input image and optimize with the crowd counting module simultaneously. Experimental results show that the proposed algorithm is effective in counting crowds under different weather types on benchmark datasets. The source code and trained models will be made available to the public.Comment: including supplemental materia

Unified Visual Relationship Detection with Vision and Language Models

This work focuses on training a single visual relationship detector predicting over the union of label spaces from multiple datasets. Merging labels spanning different datasets could be challenging due to inconsistent taxonomies. The issue is exacerbated in visual relationship detection when second-order visual semantics are introduced between pairs of objects. To address this challenge, we propose UniVRD, a novel bottom-up method for Unified Visual Relationship Detection by leveraging vision and language models (VLMs). VLMs provide well-aligned image and text embeddings, where similar relationships are optimized to be close to each other for semantic unification. Our bottom-up design enables the model to enjoy the benefit of training with both object detection and visual relationship datasets. Empirical results on both human-object interaction detection and scene-graph generation demonstrate the competitive performance of our model. UniVRD achieves 38.07 mAP on HICO-DET, outperforming the current best bottom-up HOI detector by 14.26 mAP. More importantly, we show that our unified detector performs as well as dataset-specific models in mAP, and achieves further improvements when we scale up the model. Our code will be made publicly available on GitHub.Comment: Accepted to ICCV 2023. Code is available at https://github.com/google-research/scenic/tree/main/scenic/projects/univr

Virtual Guidance as a Mid-level Representation for Navigation

In the context of autonomous navigation, effectively conveying abstract navigational cues to agents in dynamic environments poses challenges, particularly when the navigation information is multimodal. To address this issue, the paper introduces a novel technique termed "Virtual Guidance," which is designed to visually represent non-visual instructional signals. These visual cues, rendered as colored paths or spheres, are overlaid onto the agent's camera view, serving as easily comprehensible navigational instructions. We evaluate our proposed method through experiments in both simulated and real-world settings. In the simulated environments, our virtual guidance outperforms baseline hybrid approaches in several metrics, including adherence to planned routes and obstacle avoidance. Furthermore, we extend the concept of virtual guidance to transform text-prompt-based instructions into a visually intuitive format for real-world experiments. Our results validate the adaptability of virtual guidance and its efficacy in enabling policy transfer from simulated scenarios to real-world ones.Comment: Tsung-Chih Chiang, Ting-Ru Liu, Chun-Wei Huang, and Jou-Min Liu contributed equally to this work; This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl