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4th International conference on Telecommunications, Optics, and Computer Science, TOCS 2023

作者： Sun, Lijuan Wei, Jiansheng Li, xiangwei Zhao, xiaoqiang Tang, Jiawei School of Computer Science and Artificial Intelligence Lanzhou Institute of Technology Lanzhou730000 China Li County Rhubarb technology Co. LTD Gansu province Li County Industrial Park Longnan Longnan730000 China

ISBN: (数字)9781510679849

ISBN: (纸本)9781510679832

In order to improve the processing quality of traditional Chinese medicine rhubarb and control the processing process of rhubarb in real time, a programmable logic control system with Ethernet and Internet of things remote communication function was designed, and the corresponding programming software and configuration software were developed, which had important engineering application value for the transformation and upgrading of traditional processing equipment of rhubarb. In this paper, based on 5G Narrowband Internet of Things (NBiot) technology and industrial Ethernet technology, a programmable logic control system with remote wireless communication function is designed, and the supporting programming software platform is developed to connect to the cloud wireless network. The research results were tested in Li County Rhuai Technology Co., LTD. The rhuai production yield was increased by 30%, the processing quality was effectively improved, and the effectiveness of the rhuai processing process control was guaranteed. In addition, the results can provide reference for the research and development of intelligent control systems in traditional Chinese medicine preparation industry, and realize the large-scale and industrialized development of traditional Chinese medicine processing equipment. © 2024 SPIE.

关键词： Internet of things

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Predicting Autonomous Vehicle Navigation Parameters via image and image-and-Point Cloud Fusion-based End-to-End Methods

Predicting Autonomous Vehicle Navigation Parameters via Imag...

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IEEE International conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Beycimen, Semih Ignatyev, Dmitry Zolotas, Argyrios Cranfield Univ Ctr Autonomous & Cyber Phys Syst SATM Bedford MK43 0AL England

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

This paper presents a study of end-to-end methods for predicting autonomous vehicle navigation parameters. image-based and image & Lidar points-based end-to-end models have been trained under Nvidia learning architectures as well as Densenet-169, Resnet-152 and Inception-v4. Various learning parameters for autonomous vehicle navigation, input models and pre-processing data algorithms i.e. image cropping, noise removing, semantic segmentation for image data have been investigated and tested. The best ones, from the rigorous investigation, are selected for the main framework of the study. Results reveal that the Nvidia architecture trained image & Lidar points-based method offers the better results accuracy rate-wise for steering angle and speed.

关键词： Training Laser radar Recurrent neural networks Navigation Prediction algorithms Data models Land vehicles

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"Core Strength" of Dance Lala Training Considering the Body Motion Tracking Video and Predictive Model

"Core Strength" of Dance Lala Training Considering the Body ...

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2022 International conference on Electronics and Renewable systems, ICEARS 2022

作者： Zhao, Jing Xi'an International Studies University Sports Department Xi'an710128 China

ISBN: (纸本)9781665484251

"Core Strength"of the dance Lala training considering the body motion tracking video and predictive model is studied in this paper. When constructing the training sample template, it is necessary to set the value of the number of poses in advance. Too small a value will increase the error of the training action behavior template and reduce the recognition accuracy;too large a value will also increase the complexity of model training, training and recognition execution time will also increase. The function of the pose estimation module is to complete the description and expression of the specific posture of the human body at the current moment according to the frame image. After processing by the pose estimation module, each frame in the sequence can be calculated to obtain a feature vector, so that the image sequence can be converted into a feature vector sequence for the next step of action recognition. With these models, the applications on the dance Lala training is tested. © 2022 IEEE.

关键词： Video signal processing

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IoT-Based Edge Computing and image processing for Occupancy Detection 11

IoT-Based Edge Computing and Image Processing for Occupancy ...

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2023 IEEE 11th International conference on Information, Communication and Networks, ICICN 2023

作者： Guo, Chengpeng Hu, Bintao Zhang, Wenzhang Gao, Yuan Liu, Hengyan Xi'An Jiaotong-Liverpool University School of AI and Advanced Computing Suzhou China Xi'An Jiaotong-Liverpool University School of Internet of Things Suzhou China Shanghai University School of Communication and Information Engineering Shanghai China

ISBN: (纸本)9798350314014

A primary strategy for the energy-efficient operation of commercial office buildings is to offer dynamic building services, including lighting, heating, ventilating, and air conditioning (HVAC). Therefore, it is necessary to propose an effective and high-accuracy guaranteed real-time video occupancy detection optimisation algorithm according to a model predictive control (MPC) system. In this paper, we propose a YOLOv5 occupation detection algorithm to enhance the accuracy of making informed decisions and control actions on video occupancy detection systems, where the MPC system uses multiple cameras to capture and average the number of people, and a DHTII sensor will be used to measure temperature and humidity and sent to the MPC program through a Raspberry Pi-based script. Simulation results demonstrate the effectiveness of deep learning-based methods in enhancing the accuracy and real-time performance of video occupancy detection systems, paving the way for more efficient and intelligent building management solutions. © 2023 IEEE.

关键词： Model predictive control

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Enhancing Patient Privacy in IoT-Enabled Intelligent systems: A Deep and Broad Learning-Based Efficient Encryption Network 8

Enhancing Patient Privacy in IoT-Enabled Intelligent Systems...

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8th International conference on Smart Internet of Things

作者： Zhu, xiaoqiang Liu, Jiqiang Zhang, Dalin Li, Lingkun Wang, Nan Beijing Jiaotong Univ Sch Cyberspace Sci & Technol Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing 100044 Peoples R China

ISBN: (纸本)9798350366457;9798350366440

The rapid development of the Internet of Things (IoT) is enabling a wide range of applications in intelligent medical systems. Among others, medical imaging equipment produces sensitive user privacy information, however, current solutions from academia and industry often neglect the importance of secure communication mechanisms. There are open research challenges such as low real-time processing and poor security, even when using cryptography. This paper proposes EDBNet, an efficient encryption network based on deep and broad learning to improve patient privacy for medical images. To be specific, a four-layer convolutional neural network is employed to extract the horizontal and vertical factors and utilize broad learning to guide the training model to obtain two feature matrices. The training process includes pre-training and fine-tuning, with the open-source COVID-CT-Dataset enabling dual-stream encryption. To further enhance ciphertext image security in a privacy-protected environment, chaotic cryptography is utilized to consummate the encryption network, which includes scrambling and diffusion combing with the SHA3-256 algorithm. The proposed EDBNet is evaluated by extensive experiments, which show that it outperforms several state-of-the-art algorithms, such as the average cipher entropy of 7.9971, encryption quality of 248, and encrypted/decrypted time of around 1 second.

关键词： IoT image encryption Privacy protection Machine learning Chaos

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A Semantic Feature-Driven Multiscale Grid Dehazing Method

A Semantic Feature-Driven Multiscale Grid Dehazing Method

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6th International conference on Intelligent Computing and Signal processing (ICSP)

作者： Hao Zhang Hongmei Jin Zhanli Li Ning He Xi'an University of Science and Technology Xi'an China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Aiming at the problems of incomplete dehazing and detail distortion in the dehazing process of existing algorithms, an end-to-end dehazing method SemanticGridDehazeNet is proposed, which is semantic feature-driven and combines multi-scale feature fusion. The method consists of three modules: preprocessing, backbone, and post-processing. First, preprocessing is performed using a newly designed residual dense fusion block (RDFB) and convolutional layer. The Backbone module is based on the extended GridN et, which contains three processing scales, and semantic features extracted based on VGG16 Net are added in front of each scale to enhance learning. Each scale contains five RDFB blocks to fuse the feature map to the last column. Then an RDFB post-processing module improves the output quality. The main innovations include: (1) the introduction of the RDFB fusion module to focus on haze features, dynamically adjust the level of attention paid to channel or pixel locations to more effectively learn and utilize key information about the haze distribution in the input data; (2) Semantic features are integrated into the image dehazing process by incorporating semantic features extracted from VGG16 Net before each processing scale. This enhances multi-scale learning and enables more accurate focus on haze features, improving dehazing effectiveness. Experimental results demonstrate superior performance in target enhancement, robustness, and generalization. The method effectively removes residual hzae and addresses detail distortion issues.

关键词： Measurement Technological innovation image color analysis Fuses Semantics Signal processing algorithms Feature extraction Distortion Robustness Real-time systems

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Medical image super-resolution algorithm based on multi-scale feature aggregation 2

Medical image super-resolution algorithm based on multi-scal...

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2024 International conference on Medical Artificial Intelligence

作者： Han, xinyu Gong, Xun Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Minist Educ Engn Res Ctr Sustainable Urban Intelligent Transp Chengdu 611756 Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Chengdu 611756 Peoples R China Southwest Jiaotong Univ Mfg Ind Chains Collaborat & InformationSupport Te Chengdu 611756 Peoples R China

ISBN: (纸本)9798350377620;9798350377613

Ultrasound images are vital for medical diagnostics but often suffer from information loss and blurred details due to limitations in imaging systems and sensor technologies. Many researchers have proposed super-resolution algorithms to enhance medical images. But existing super-resolution methods struggle with the uneven distribution of noise and clarity. To address this, we propose a super-resolution algorithm for medical images based on multi-scale feature aggregation Leveraging the architecture of Unet as our primary framework, our method enhances output details through multi-scale hole convolutions. Taking into consideration the characteristics of ultrasonic images, we propose a frequency domain-based module to enhances edge information while effectively denoising the image. Furthermore, to improve the quality of the output images, we introduce an image processing nodule grounded in global information. The module ensures clarity while considering the overarching context, thereby preserving global consistency and enhancing output quality. We experiment on three datasets to demonstrate the effectiveness of our model. Additionally, significant improvements in medical image segmentation are observed, proving the practicability of our proposed approach.

关键词： Ultrasound images super-resolution multi-scale feature aggregation frequency-domain global information

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image Frequency Separation Residual Network for End-to-end RAW to RGB Mapping 5

Image Frequency Separation Residual Network for End-to-end R...

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5th IEEE International conference on Artificial Intelligence Circuits and systems, AICAS 2023

作者： Dong, Mengchuan Zhou, Wei Pang, Cong Zhang, xiangyu Lou, xin ShanghaiTech University Shanghai China

ISBN: (纸本)9798350332674

Due to the limitations of hardware specification of smartphones' camera system, there is still a visible gap in imaging quality between smartphones and digital singlelens reflex (DSLR) cameras. Sophisticated learning-based image processing becomes a promising solution to close this gap. In this paper, we propose an image Frequency Separation Residual Network (IFS Net) to perform the end-to-end RAW to RGB image mapping. Different from existing methods that directly train the input image and the ground truth image one-to-one as a whole, our proposed method first divides the input image and the ground truth into high-frequency and low-frequency parts by discrete wavelet transform (DWT). These two parts are then trained separately using different networks for details and global information, and finally synthesized into the output image using inverse DWT. Experimental results show that the proposed IFS Net outperforms other existing algorithms in both PSNR and SSIM. Visual comparison shows that the images produces by IFS Net preserves more details and look close to that captured by DSLR cameras. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Trajectory Flow Matching with Applications to Clinical Time Series Modeling 38

Trajectory Flow Matching with Applications to Clinical Time ...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Zhang, xi Pu, Yuan Kawamura, Yuki Loza, Andrew Bengio, Yoshua Shung, Dennis L. Tong, Alexander McGill University Canada Mila - Quebec AI Institute Canada Yale School of Medicine United States School of Clinical Medicine University of Cambridge United Kingdom Université de Montréal Canada CIFAR Canada

Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medicine. Neural stochastic differential equations (Neural SDEs) are an attractive modeling technique for this problem, which parameterize the drift and diffusion terms of an SDE with neural networks. However, current algorithms for training Neural SDEs require backpropagation through the SDE dynamics, greatly limiting their scalability and stability. To address this, we propose Trajectory Flow Matching (TFM), which trains a Neural SDE in a simulation-free manner, bypassing backpropagation through the dynamics. TFM leverages the flow matching technique from generative modeling to model time series. In this work we first establish necessary conditions for TFM to learn time series data. Next, we present a reparameterization trick which improves training stability. Finally, we adapt TFM to the clinical time series setting, demonstrating improved performance on four clinical time series datasets both in terms of absolute performance and uncertainty prediction, a crucial parameter in this setting. © 2024 Neural information processing systems foundation. All rights reserved.

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Denoising algorithm of OCT images via sparse representation based on noise estimation and global dictionary

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OPTICS EXPRESS 2022年第4期30卷 5788-5802页

作者： Zhang, xi Li, Zhongliang Nan, Nan Wang, xiangzhao Chinese Acad Sci Shanghai Inst Opt & Fine Mech Lab Informat Opt & Optoelect Technol Shanghai 201800 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China

Optical coherence tomography (OCT) is a high-resolution and non-invasive optical imaging technology, which is widely used in many fields. Nevertheless, OCT images are disturbed by speckle noise due to the low-coherent interference properties of light, resulting in significant degradation of OCT image quality. Therefore, a denoising algorithm of OCT images via sparse representation based on noise estimation and global dictionary is proposed in this paper. To remove noise and improve image quality, the algorithm first constructs a global dictionary from high-quality OCT images as training samples and then estimates the noise intensity for each input image. Finally, the OCT images are sparsely decomposed and reconstructed according to the global dictionary and noise intensity. Experimental results indicate that the proposed algorithm efficiently removes speckle noise from OCT images and yield high-quality images. The denoising effect and execution efficiency are evaluated based on quantitative metrics and running time, respectively. Compared with the mainstream adaptive dictionary denoising algorithm in sparse representation and other denoising algorithms, the proposed algorithm exhibits satisfying results in terms of speckle-noise reduction as well as edge preservation, at a reduced computational cost. Moreover, the final denoising effect is significantly better for sets of images with significant variations in noise intensity. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： image processing image quality Imaging systems Light properties Optical computed tomography Optical imaging

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