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Development of a Facial Recognition Pantograph Drawing robot 3

Development of a Facial Recognition Pantograph Drawing Robot

引用

3rd international conference on robotics, Electrical and signal processing Techniques, ICREST 2023

作者： Giles, Daniel Glenn, Payton Burnell, Travis Flynn, Skylar Noorani, R. Loyola Marymount University Mechanical Engineering Department Los Angeles United States

ISBN: (纸本)9798350346435

This paper describes the design and application of a solar powered drawing robot that implements face detection, image to sketch conversion, and wireless communication between the robot and the main computer. The robot has the capability to detect and capture a picture of a human face, convert the image to a sketch in the form of a series of lines, and physically draw the image on a whiteboard. Additionally, the robot can draw any selected image stored on the main computer, apply or remove shading from the image, move to a specified location away from the sketch to provide a clear area for the user to erase the board, and can draw diagnostic test rectangles to analyze the mechanical performance of the system. These robotic functions were implemented by first determining the angles and geometry of the arms required to locate a given x-y coordinatepoint. This was done using properties of triangles, as well as the known physical limitations of the pantograph's size. A CAD model of the design was created to incorporate both the calculated geometry and purchased parts, such as the servo motors, axles, and bearings. Existing open-source libraries were used to aid in facial recognition and the conversion of images to a sketch of x-y points. By utilizing the known geometry of the robot, the pen was programmed to move to specified points based on the angles of the two servo motors. © 2023 IEEE.

关键词： robots

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Realtime Trajectory Smoothing with Neural Nets 39

Realtime Trajectory Smoothing with Neural Nets

引用

IEEE international conference on robotics and Automation (ICRA)

作者： Fujii, Shohei Pham, Quang-Cuong Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Singapore DENSO CORP Kariya Aichi Japan Eureka Robot Singapore Singapore

ISBN: (纸本)9781728196817

In order to safely and efficiently collaborate with humans, industrial robots need the ability to alter their motions quickly to react to sudden changes in the environment, such as an obstacle appearing across a planned trajectory. In Realtime Motion Planning, obstacles are detected in real time through a vision system, and new trajectories are planned with respect to the current positions of the obstacles, and immediately executed on the robot. Existing realtime motion planners, however, lack the smoothing post-processing step - which are crucial in sampling-based motion planning - resulting in the planned trajectories being jerky, and therefore inefficient and less human-friendly. Here we propose a Realtime Trajectory Smoother based on the shortcutting technique to address this issue. Leveraging fast clearance inference by a novel neural network, the proposed method is able to consistently smooth the trajectories of a 6-DOF industrial robot arm within 200 ms on a commercial GPU. We integrate the proposed smoother into a full vision-Motion Planning-Execution loop and demonstrate a realtime, smooth, performance of an industrial robot subject to dynamic obstacles.

关键词： Point cloud compression Smoothing methods Torque Service robots Graphics processing units Real-time systems Trajectory

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A toolbox for neuromorphic perception in robotics 8

A toolbox for neuromorphic perception in robotics

引用

8th international conference on Event-Based Control, Communication and signal processing (EBCCSP)

作者： Dupeyroux, Julien Stroobants, Stein de Croon, Guido C. H. E. Delft Univ Technol Micro Air Vehicle Lab MAVLab Delft Netherlands Delft Univ Technol Fac Aerosp Engn Micro Air Vehicle Lab Delft Netherlands

ISBN: (数字)9781665453493

ISBN: (纸本)9781665453493

The third generation of artificial intelligence (AI) introduced by neuromorphic computing is revolutionizing the way robots and autonomous systems can sense the world, process the information, and interact with their environment. Research towards fulfilling the promises of high flexibility, energy efficiency, and robustness of neuromorphic systems is widely supported by software tools for simulating spiking neural networks, and hardware integration (neuromorphic processors). Yet, while efforts have been made on neuromorphic vision (event-based cameras), it is worth noting that most of the sensors available for robotics remain inherently incompatible with neuromorphic computing, where information is encoded into spikes. To facilitate the use of traditional sensors, we need to convert the output signals into streams of spikes, i.e., a series of events (+ 1,- 1) along with their corresponding timestamps. In this paper, we propose a review of the coding algorithms from a robotics perspective and further supported by a benchmark to assess their performance. We also introduce a ROS (robot Operating System) toolbox to encode and decode input signals coming from any type of sensor available on a robot. This initiative is meant to stimulate and facilitate robotic integration of neuromorphic AI, with the opportunity to adapt traditional off-the-shelf sensors to spiking neural nets within one of the most powerful robotic tools, ROS.

关键词： Neuromorphic engineering Neural networks signal processing algorithms signal processing robot sensing systems Sensor systems Robustness

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HiE-VL: A Large vision-Language Model with Hierarchical Adapter for Handwritten Mathematical Expression Recognition

HiE-VL: A Large Vision-Language Model with Hierarchical Adap...

引用

2025 IEEE international conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Guo, Hong-Yu Yin, Fei Xu, Jian Liu, Cheng-Lin School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Large vision-Language Models (LVLMs) have shown impressive capabilities across various domains, but existing LVLMs have limited performance in dense perception and structured learning problems, such as Handwritten Mathematical Expression Recognition (HMER). The primary challenges stem from the complexity of formula images comprising multiple symbols and complicated inter-symbol relationships. This poses difficulties to LVLMs with locality insensitive visual encoders and structure-agnostic vision-language projectors. To overcome these challenges, we propose HiE-VL, the first LVLM for HMER containing: (1) a primitive-aware high-resolution visual encoder, (2) a hierarchical adapter, (3) a math-context enhanced large language model (LLM). Specifically, the adopted visual encoder allows locating and recognizing symbols in complex formula images. The hierarchical adapter functions as a vision-language projector to progressively capture primitive and structure information for facilitating expression decoding. The whole model is optimized in a two-stage training pipeline. In experiments on two benchmark datasets of HMER, our model achieves significantly higher performance than existing LVLMs like GPT-4V and state-of-the-art HMER models. Our codes are available at https://***/guohy17/HiE-VL. © 2025 IEEE.

关键词： handwritten mathematical expression recognition instruction tuning pre-training vision-language model

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Balance Control of Quadruped robot under External Disturbance using Quadratic Programming and Impedance Control 5

Balance Control of Quadruped Robot under External Disturbanc...

引用

5th international conference on Intelligent Control, Measurement and signal processing, ICMSP 2023

作者： Wang, Liufeng Li, Jun University of Science and Technology of China Department of Automation Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9798350336030

Legged robots are widely used in complex environments because of their strong motion flexibility and environmental adaptability. However, uncertain external disturbances in complex environments will bring adverse effects on the stability and motion state of robots in the actual working process. For quadruped robot operating under external disturbance in complex environment, a dynamic balance control framework based on QP (quadratic programming) method and impedance control is proposed in this paper. The whole robot control system adopts dynamic control scheme. We use the single rigid body model to simplify the dynamics model of the quadruped robot, and use QP with friction cone constraint to optimize the desired foot contact forces, and achieve the optimal distribution of foot contact force. Impedance control is used to smoothly control the swing leg of a quadruped robot, which greatly reduces the impact of the swing leg and the ground on the robot body. In this paper, the A1 quadruped robot of Unitree Technology was simulated by Webots. The experiments showed that A1 quadruped robot could resist 60N lateral impact force and continuous external drag force, which verified the effectiveness and robustness of the proposed control algorithm. © 2023 IEEE.

关键词： Quadratic programming

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Semi-supervised classification method for PolSAR Based on vision transformer (ViT) 4

Semi-supervised classification method for PolSAR Based on vi...

引用

4th international conference on signal Image processing and Communication, ICSIPC 2024

作者： Li, Xiangshen Yu, Jie Han, Lihua College of Resource Environment and Tourism Capital Normal University Beijing100048 China Beijing Laboratory of Water Resources Security Capital Normal University Beijing100048 China State Key Laboratory Incubation Base of Urban Environmental Process and Digital Simulation Capital Normal University Beijing100048 China Land Satellite Remote Sensing Application Center of MNR Beijing100048 China

ISBN: (纸本)9781510682467

Semi-supervised classification methods generate pseudo-labels from unlabeled data, where pseudo-labels' precisio n is vital for successful classification outcomes. Addressing the challenge of inaccuracies in pseudo-label catego ries during training, this paper introduces a semi-supervised classification strategy for PolSAR images, leveragin g the vision Transformer (ViT) technology. In this study, the Simple Linear Iterative Clustering (SLIC) method was applied to create superpixel blocks with distinct boundaries and organized structures, facilitating the generat ion of pseudo-labels with a correctness rate of 85.9%. This paper introduces the vision Transformer (ViT) net work, capitalizing on its multi-head attention mechanism. This approach enhances global image information extr action while simultaneously addressing local details for pseudo-label training. Specifically, on the Flevoland data set, an increase in pseudo-label category accuracy of 7.09% and 5.47% was achieved. For the Wuhan Tongshu n River dataset, improvements of 2.44% and 3.41% in pseudo-label category accuracy were recorded, thereby e levating the precision of PolSAR semi-supervised classification. © 2024 SPIE.

关键词： Image enhancement

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Can We Learn to Compress RF signals? 7

Can We Learn to Compress RF Signals?

引用

7th international Balkan conference on Communications and Networking (BalkanCom)

作者： Kaasaragadda, Yagna Rodriguez, Armani Kokalj-Filipovic, Silvija Rowan Univ Glassboro NJ 08028 USA

ISBN: (纸本)9798350365962;9798350365955

The AI based on radio spectrum sensing is being deployed to detect and classify various interference sources and optimize spectrum allocation in next-generation cellular networks. Radio frequency (RF) spectrum sensing for AI must generate large quantities of RF data whose transport to the AI in the Cloud and at the Edge will incur significant bandwidth and latency costs. Can these quantities be compressed without affecting the utility of the cellular AI models? Our deep learned compression (DLC) model, named HQARF and based on learned vector quantization (VQ), compresses and reconstructs complex-valued samples of RF signals comprised of different modulation classes. In this paper we analyze how the salient (signal-processing) properties of the RF signal reconstructions by HQARF are modified due to incremental compression, and how this affects the accuracy of an AI model trained to infer the signal's modulation class. This analysis is also important as many advanced AI models developed for vision, such as Stable/VQ Diffusion, are trained on the quantized latent representation of the data. Understanding effects of learned RF quantization may help leverage those advanced models in the RF domain.

关键词： Quantization (signal)

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Design of Cloud Platform Fire-Fighting Reconnaissance robot Based on STM32 and K210

Design of Cloud Platform Fire-Fighting Reconnaissance Robot ...

引用

2023 IEEE international conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Zhang, Longxiang Liu, Ting Fu, Zhixuan Chen, Guanrun Ling, Weinan City Institute Dalian University of Technology Liaoning Dalian116600 China

ISBN: (纸本)9798350309614

The article designs a fire rescue robot suitable for solving extreme and unique environments in fire scenes. In such an environment, whether it is due to the smoke obstructing vision or unfamiliarity with the fire environment, the robot can assist in rescue efforts and facilitate safe evacuation by finding safe routes. The robot utilizes STM32 as the central hub for controlling its functions, utilizes K210 for image processing, and has been designed with a dedicated cloud-based app that visualizes data, thus enabling features such as route planning, environmental surveying, human-machine interaction, gas detection, real-time monitoring, and visual flame detection. This firefighting reconnaissance robot demonstrates excellent applicability and feasibility. © 2023 IEEE.

关键词： K210 route planning STM32 visual recognition

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A Self-Adaptive Tampering Detection Algorithm Based on Image Segmentation and Feature Point Matching

A Self-Adaptive Tampering Detection Algorithm Based on Image...

引用

2023 international conference on Algorithm, Imaging processing, and Machine vision, AIPMV 2023

作者： Wang, Guokai Feng, Liuping Chi, Lingyi Zhou, Yangquan Beijing Key Laboratory of Signal and Information Processing for High-end Printing Equipments Beijing Institute of Graphic Communication Beijing China

ISBN: (纸本)9781510672444

In order to enhance the efficiency and accuracy of homologous tampering detection, image segmentation algorithms and image feature points are combined. The Simple Linear Iterative Cluster (SLIC) algorithm is employed for image segmentation. However, manually presetting the number of patches is not applicable to all images and can influence subsequent segmentation results. To achieve a more accurate detection of tampered areas, this paper proposes a self-adaptive image tampering detection algorithm. The number of image segments is determined based on image complexity, which allows the image to be segmented into semantically independent patches. Subsequently, the SIFT algorithm is employed to extract feature points for matching. Test results demonstrate that the proposed algorithm accurately localizes tampered regions and reduces algorithmic complexity. © 2024 SPIE.

关键词： Image segmentation

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Eye Disease Classification using CNN and vision Transformers 1

Eye Disease Classification using CNN and Vision Transformers

引用

1st IEEE international conference on Advances in signal processing, Power, Communication, and Computing, ASPCC 2024

作者： Prathibha, G. Acharya Nagarjuna University Anu College of Engineering and Technology Electronics and Communication Engineering Guntur India

ISBN: (纸本)9798350355093

Many people in the world are suffering with different eye Diseases. Early diagnosis of Eye diseases can save the patient from risk of losing vision. Enormous work is done on classification of diseased eyes using Machine Learning Techniques. In this work, the classification of Eye diseases are detected using features from two different architectures of CNN which are fused and classified. A total of 9825 images consisting of ACRIMA, ODIR-5K, Glaucoma, ORIGA6, Cataract and Retina diseased images are considered from which features are extracted using the different pre-trained networks. The extracted features from two different pre-trained network are fused which are further classified using the Convolution Neural Network resulting in an accuracy of 97.32% using EfficientNetB0 and VGG16. Further, the dataset is also classified using vision transformers resulting in an accuracy of 98.02% which is superior compared to the result obtained from fused features of CNN. © 2024 IEEE.

关键词： Accuracy CNN Concatenation Pre-Trained Networks Vison Transformer

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