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Sound and Complete Verification of Polynomial Networks 36

Sound and Complete Verification of Polynomial Networks

引用

36th conference on Neural Information processing systems (NeurIPS)

作者： Rocamora, Elias Abad Sahin, Mehmet Fatih Liu, Fanghui Chrysos, Grigorios G. Cevher, Volkan Ecole Polytech Fed Lausanne LIONS Lausanne Switzerland

ISBN: (纸本)9781713871088

Polynomial Networks (PNs) have demonstrated promising performance on face and image recognition recently. However, robustness of PNs is unclear and thus obtaining certificates becomes imperative for enabling their adoption in real-world applications. Existing verification algorithms on ReLU neural networks (NNs) based on classical branch and bound (BaB) techniques cannot be trivially applied to PN verification. In this work, we devise a new bounding method, equipped with BaB for global convergence guarantees, called Verification of Polynomial Networks or VPN for short. One key insight is that we obtain much tighter bounds than the interval bound propagation (IBP) and DeepT-Fast [Bonaert et al., 2021] baselines. This enables sound and complete PN verification with empirical validation on MNIST, CIFAR10 and STL10 datasets. We believe our method has its own interest to NN verification. The source code is publicly available at https://***/megaelius/PNVerification.

关键词： image recognition

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Solar Powered Auto Irrigation Monitoring System with Plant Health Indication Using image processing

Solar Powered Auto Irrigation Monitoring System with Plant H...

引用

International conference on Open Source systems and Technologies (ICOSST)

作者： Maida Saleem Khadija Nawaz Shahroz Ali Hira Ali Jamal Dept. of Electrical Engineering University of Engineering and Technology Lahore RCET Campus Gujranwala Pakistan

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

This project aims to develop a solar powered auto irrigation system that incorporates image processing techniques to monitor and maintain the health of the plants. The system utilizes solar energy to power the irrigation process, making it environmentally friendly and cost-effective. image processing algorithms are employed to analyze the visual characteristics of the plants and detect any signs of distress or disease. This information is then used to automatically adjust the irrigation schedule and provide necessary nutrients to ensure optimal plant health. Furthermore, the system integrates image processing techniques to analyze the health of the plants. images of the plants are captured using a camera module and processed to detect any signs of diseases or abnormalities. The system then provides real-time feedback on the plant health status. Through the implementation of this project, the efficiency and effectiveness of irrigation systems can be improved, leading to better crop yields and reduced water usage.

关键词： Temperature sensors Training Irrigation Visualization image processing Valves Sensor systems Real-time systems Sensors Monitoring

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image Compressive Sensing With Adaptive Sampling by Median Filtering

Image Compressive Sensing With Adaptive Sampling by Median F...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yanfeng Wu Chen Hui Ronghua Liao Shaohui Liu Debin Zhao Department of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep unfolding compressive sensing (CS) has experienced remarkable advancements. However, there still exist two challenges: (1) Many algorithms either use uniform block-based sampling, which ignore the fact that the content of different blocks is different, or allocate the sampling rate referring to complete signal before CS sampling, which is not always feasible in real-world scenarios. (2) Traditional CNN is difficult to capture broader contextual priors during iterative recovery. In this paper, we propose a novel network ASMFNet to solve the above two issues. Specifically, to address the first issue, we introduce a dual-branch network featuring a basic sampling branch to acquire reference image and an adaptive sampling branch by median filtering for allocating remaining sampling rate adaptively. For the second problem, we use Swin Transformer and feature fusion block to increase the feature interactions. Experimental results demonstrate that our proposed method outperforms existing methods.

关键词： Adaptive systems Filtering Signal processing algorithms Signal processing Transformers Iterative algorithms Sensors Speech processing Compressed sensing image reconstruction

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A Review on Designing of Memory Computing Architecture for image Enhancement in AI Applications 1

引用

16th International conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2023

作者： Radhika, C. Ganesh, G.V. Babu, P. Ashok Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of ECE Institute of Aeronautical Engineering Telangana Dundigal Hyderabad India

ISBN: (数字)9783031364020

ISBN: (纸本)9783031364013

This paper presents the random spray retinex (RSR) algorithm, which is an efficient image enhancement technique with the potential to improve image quality. However, the computational complexity of the algorithm, as well as the hardware and memory units it requires, may limit its applicability in contexts with constrained resources, such as Internet of Things systems. image augmentation is increasingly important for improving the performance of many modern applications, including artificial intelligence (AI). Our research confirms that RSR can enhance segmentation accuracy. To overcome the limitations of the RSR algorithm, we propose a memristor-based in-memory computing architecture, utilizing Resistive RAM (RRAM) technology, which provides low-power solutions at a low cost. We evaluate the performance of various RSR algorithms in terms of processing time on this architecture. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Memristors

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image Target Detection Algorithm Based on Computer Vision Technology

Image Target Detection Algorithm Based on Computer Vision Te...

引用

2022 International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2022

作者： Yuan, Haidi Anhui Sanlian College Basic Experiment Teaching Center Anhui Hefei China

ISBN: (数字)9781665490092

ISBN: (纸本)9781665490092

With the rapid development of intelligent systems and the advent of the era of big data, the continuous development of computers is being promoted. Exporting and tracking moving targets in video images is one of the most important research contents of computer vision. It combines many advanced technologies in the field of computing, such as image processing, pattern recognition, automatic control and artificial intelligence, and is widely used in intelligent surveillance. In various fields such as traffic control, machine intelligence and medical diagnosis, visual effects are obtained through image or image processing. Record videos from the computer and perform specific mechanical tasks. In terms of intelligent tracking, as the demand for applications in various complex environments continues to grow, how to improve the robustness and accuracy of moving target tracking and tracking algorithms has become the focus of ongoing target tracking research. This paper studies the image target detection algorithm based on computer vision technology. Firstly, the literature research method is used to summarize the existing problems of image target detection based on computer vision technology and the existing algorithms. The experiment is used to analyze the image target based on computer vision technology. The detection algorithm is verified, and the error rate of image target detection of the algorithm proposed in this paper is compared. According to the experimental results, it can be seen from Figure 1 that in experiment 1, the target detection of the GMM-STMRF algorithm is more accurate than other methods based on the calculation of the false detection rate. The maximum false detection rate is only 2.3%, and the other algorithms have 5.4%-11.1% false detection rate The GMM-STMRF algorithm increases the multi-frame calculation in the time dimension, so the calculation time has increased. algorithms such as GMM and MeanShift need to estimate the multi-frame parameters, an

关键词： Target tracking

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ISAR IMAGING OF IFDS ECHO BASED ON GPU PARALLEL processing

ISAR IMAGING OF IFDS ECHO BASED ON GPU PARALLEL PROCESSING

引用

IET International Radar conference 2023, IRC 2023

作者： Lei, Junfeng Liu, Tao Tian, Biao School of Electronics and Communication Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen China

ISBN: (纸本)9781839539954

The Intermediate Frequency Direct Sampling (IFDS) mode in Inverse Synthetic Aperture Radar (ISAR) imaging preserves the original information of the echo signal and radar system characteristics. However, the massive data accompanying this mode imposes considerable computational burdens on subsequent steps. As a result, traditional ISAR imaging algorithms and implementations face challenges in achieving fast, real-time imaging in IFDS mode. This paper presents the design and implementation of an IFDS echo ISAR imaging system based on parallel computing using Graphic processing Unit (GPU). The efficiency and effectiveness of the proposed system were validated on the simulated data. Significant performance improvements compared to traditional CPU-bound ISAR imaging systems were demonstrated in the results. These improvements were achieved with minimal impact on the final quality of the two-dimensional ISAR image. © The Institution of Engineering & Technology 2023.

关键词： Radar imaging

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Research on an Innovative Algorithm for Optimizing Intelligent image Data systems Based on Deep Learning

Research on an Innovative Algorithm for Optimizing Intellige...

引用

IEEE International conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Xiaonan Dong Yingbin Song Wuhan Technology and Business University Wuhan Hubei China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

This paper proposes an innovative algorithm for optimizing intelligent image data systems based on deep learning. The algorithm combines image feature extraction, data preprocessing and efficient optimization strategies to improve the performance and accuracy of image data processing systems. First, by designing a deep CNN architecture, important features in the image are extracted to achieve efficient completion of image recognition and classification tasks. Subsequently, a new multi-level data processing method is proposed, which can optimize image data at different levels, thereby improving processing speed and reducing noise interference. Through a series of simulation experiments, the results show that the image classification accuracy of the algorithm is improved by about 12 % , from 85.6% of the traditional method to 97.3%. In addition, the processing efficiency is improved by about 20%, the data processing time is reduced from 2.5 seconds of the traditional method to 2 seconds, and the stability of the system is significantly enhanced by introducing optimization strategies, and the stability is improved by about 18%. The optimized algorithm shows significant advantages in both accuracy and efficiency, meeting the needs of efficient intelligent image processing systems.

关键词： Deep learning Accuracy image recognition Noise Data processing Feature extraction Stability analysis Classification algorithms Optimization image classification

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Application of 3D image Virtual Reconstruction in Multimedia Element Visual Communication Art Design 4

Application of 3D Image Virtual Reconstruction in Multimedia...

引用

4th IEEE Annual Flagship India Council International Subsections conference, INDISCON 2023

作者： Wang, Jing Wang, Xiaopeng The College of Arts College of Humanities & Sciences of Northest Normal University Changchun China Changchun Institute of Optics Fine Mechanies and Physics Chinese Academy of Seience The Space Department Changchun China

ISBN: (纸本)9798350333558

Aiming at the problem of large noise and artifacts generated by the first 3D image virtual reconstruction system, a 3D image virtual reconstruction system based on visual communication technology is proposed. In this paper, cy25400 chip and cdcvf2510 chip are used to optimize the original hardware model, expand the clock module, and improve the data processing efficiency;In the two-dimensional image, read the main data, display it in three-dimensional form from the converted data, select special areas, extract image features, count the main points of the joint, and complete the main writing;Using visual communication tools to feed back the visual content of three-dimensional images, a virtual reconstruction system of three-dimensional images is created. The effectiveness of the proposed method is tested by comparison. The experimental results show that the reconstruction accuracy of the system is always higher than that of the laser Doppler image 3D reconstruction system based on virtual machine and the DICOM image 3D reconstruction system based on MC and RC algorithms. The maximum reconstruction accuracy of this system is as high as 0.97. The user satisfaction of the system is very important, and the satisfaction is higher than 0.95. Result finding that compared with the reconstruction results of the old methods, the virtual construction of 3D images using visual communication technology can remove noise, make the image area clear, and meet the needs of customers. © 2023 IEEE.

关键词： Visual communication

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Transforming Medical Waste Management Through IoT and Machine Learning: A Path Towards Sustainability

Transforming Medical Waste Management Through IoT and Machin...

引用

Computing conference

作者： Nasir, Nahin Hossain, Mohammed Shahriar Islam, Md Saiful Islam, Md Arifiul Islam, Md Manirul Amer Int Univ Bangladesh Dhaka 1229 Bangladesh

ISBN: (纸本)9783031622793;9783031622779;9783031622762

The systematic process of detecting, gathering, and disposing of all waste in a specific manner so as not to jeopardize human or environmental life is known as waste management. If not correctly managed, medical waste, which includes items like old needles, sharps, and infectious materials, poses a serious risk to the environment and the general public's health, especially in developing and underdeveloped countries. In this study, we used IoT and machine learning technologies to automate waste identification, information tracking, and monitoring, improve the accuracy and efficiency of waste bins, and enable real-time monitoring and analysis of waste information. By utilizing smart waste bins fitted with sensors and machine learning algorithms to automatically detect and classify various forms of waste, IoT and machine learning are being applied to the management of medical waste. These intelligent bins can then notify waste management staff when they need to be emptied and can offer useful information on waste streams for analysis and practice improvement. Only persons who have been authenticated will be permitted to collect medical waste, and the system will keep a record of all data. Using IoT and machine learning technologies, we attempted to reduce the danger of unintentional exposure to hazardous materials, which enhanced overall public health and safety in addition to increasing the effectiveness of medical waste management. Overall, compared to current manual medical waste management systems, the convergence of IoT and machine learning has the potential to significantly increase the sustainability and efficiency of medical waste management, reducing the environmental impact of healthcare operations and enhancing public health.

关键词： IoT Machine learning NodeMCU ESP8266 Ultrasonic MQ-135 Rfid-rc522 HX711 MQTT Node-RED image processing Medical waste management

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Content-based image Retrieval in Cyber-Physical System Modeling of Robots 11th

Content-based Image Retrieval in Cyber-Physical System Model...

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11th International conference on Recent Trends in Computing, ICRTC 2022

作者： Anantha Prabha, P. Subashree, B. Deva Priya, M. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9789811988240

Cyber-Physical System (CPS) is used in industries and automated plants as they have modern administration abilities and are capable of performing real-time processing in a distributed architecture. In mechanical plants, they are expected to perform image recognition and retrieval, and other Machine Learning (ML) processes for efficient functioning and screening of assembling units and products. The existing strategies like remote sensing retrieval and the Gaussian mixture model for image Retrieval (IR) are not suitable for huge datasets. The drawbacks of the existing strategies can be eliminated based on features of data by using Improvised Density Scale-Invariant Feature Transform (ID-SIFT). This approach mainly focuses on geometrical features like color, texture, size and shape-based image matching offering an improved accuracy of 89%. This approach is relatively efficient when compared to existing algorithms used in IR. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical System

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