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检索条件"任意字段=International Conference on Signal Image Technology and Internet Based Systems"

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Explaining Representations in Correlation-based Deep Multiview Representation Learning

Explaining Representations in Correlation-based Deep Multivi...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Maurice Kuschel Amr Alkhatib Tanuj Hasija Henrik Boström Signal and System Theory Group Paderborn University Paderborn Germany Division of Software and Computer Systems KTH Royal Institue of Technology Stockholm Sweden

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multiview representation learning techniques based on deep correlation maximization have become increasingly popular for learning meaningful and compact representations from multiview data. Even though their performance is state-of-the-art in many interpretability-critical fields, their black-box behavior poses a problem and restricts their usability. To overcome this restriction, we propose XDCCA (eXplanations for Deep Canonical Correlation Analysis), an explanation strategy using characteristic rules in combination with SHAP that exploits the inherent structure of latent spaces created by correlation maximization techniques. We demonstrate how XDCCA allows for interpreting learned representations and their correlation using real medical time series and synthetic image data.

关键词： Representation learning Correlation Time series analysis Closed box signal processing Knowledge discovery Usability Speech processing Alzheimer's disease Biomedical imaging

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AgriSense: Advanced Environmental Monitoring for Crops

AgriSense: Advanced Environmental Monitoring for Crops

引用

international conference on Communications and signal Processing

作者： S Harish B Harikumaran S Vimala Prakash T. Rajalakshmi S Vasudevan Suryakala Department of Electronics and Communication Engineering SRM Institute of science and Technology Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In addressing the challenges of irrigation in agriculture, the implementation of internet of Things (IoT) technology emerges as a promising solution. The growing need for effective water management has put farmers at a disadvantage in optimizing irrigation systems to suit crop requirements while preserving resources. A smart IoT-based irrigation system that provides an effective solution to overcome these problems. By incorporating various sensors like humidity and moisture sensors with Arduino Mega, the system continuously monitors soil conditions and crop needs in real-time. Data collected from these sensors is transmitted to the cloud via IoT connectivity for analysis and storage. A user-friendly mobile application provides remote access to this data, offering insights into soil moisture levels, environmental factors, and irrigation schedules. Moreover, the app allows manual control of the irrigation system, enabling users to remotely toggle the pump on/off as required. Crucially, the microcontroller powering the system is solar-powered, enhancing sustainability and reducing dependence on external power sources. Combining IoT technology, Arduino Mega microcontrollers, and solar energy, this system represents a scalable and environmentally conscious approach to smart irrigation, addressing the pressing need for sustainable water management in agriculture.

关键词： Irrigation Microcontrollers Crops Solar energy Sensor systems internet of Things Sustainable development

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Computer Vision based Crop Disease Prediction in Precision Agriculture

Computer Vision Based Crop Disease Prediction in Precision A...

引用

international conference on signal Processing, Communication, Power and Embedded System (SCOPES)

作者： M Shereesha G K Sandhia R Pitchai Computer Science and Engineering SRM Institute of Science andTechnology Kattankulathur Tamilnadu India Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamilnadu India Computer Science and Engineering B V Raju Institute of Technology Narsapur Telengana India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Agriculture plays a crucial role in fostering a sustainable planet worldwide. However, in case of farming, farmers still suffer from manifold difficulties including crop diseases, natural calamities, increase of fertilizer prices and financial crisis due to loss of crop yield for several reasons. technology-driven farming or precision agriculture is still far from reality as it could not reach farmers. Artificial Intelligence (AI) and internet of Things (IoT) technologies have brought unprecedented possibilities in agriculture. Therefore, of late, precision agriculture has attracted researchers and academia. Towards this end, we investigate on 54 peer-reviewed research articles to ascertain present state of the art in techniques used for crop disease prediction. This paper sheds light on various machine learning (ML) and deep learning (DL) techniques employed in the prediction of crop diseases prediction.

关键词： Precision agriculture Deep learning Manifolds Embedded systems Planets signal processing internet of Things Diseases Farming Fertilizers

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Intelligent Traffic signal Optimization via Real-Time Density Estimation and Vehicle Counting with Canny Edge Detection and YOLOv8

Intelligent Traffic Signal Optimization via Real-Time Densit...

引用

Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： S. G. Navin Nidhish R P. Ajitha Department of CSE Sathybama Instituate of Science and Technology Chennai India Sathybama Instituate of Science and Technology Chennai India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

By and large, traffic congestion is a chronic problem in urban transportation systems, because it causes elongation of travel time, additional fuel consumption, and amplified environmental pollution. However, traditional traffic signal control systems, such as fixed time interval control or rudimentary sensors, adapt poorly to real time traffic conditions, making problems worse. An intelligent traffic signal optimization system based on real time traffic density estimation and vehicle counting is proposed by this research. The system implements Canny edge detection to enable traffic density estimation, and YOLOv8 for distinguishing and counting vehicles. The system attempts to reduce congestion as well as improve traffic flow by dynamically adapting traffic signal timings to real time data. To ensure robustness of the model in various traffic scenarios, real world traffic video datasets will be utilized in order to train and evaluate the model. Results from this study would greatly help develop traffic management systems to enhance urban transportation and could offer a scalable and economical solution to transportation problems in an urban environment.

关键词： Adaptation models image edge detection Urban areas Estimation Transportation Streaming media Real-time systems Timing Optimization Traffic congestion

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Automatic Tumour Segmentation Network based on PLHN Architecture and Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Automatic Tumour Segmentation Network Based on PLHN Architec...

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Integrated Circuits and Communication systems (ICICACS), IEEE international conference on

作者： Yaoxuan Xie Jiajian Li Chenyang Rong Yan Cheng College of Software Taiyuan University of Technology Taiyuan China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Tumor diagnosis often requires hemodynamic information to be judged. DCE-MRI technology can record and compare information well and has become an important tool in the diagnosis process. However, at this stage, the automatic segmentation technology for tumors is still not perfect, which is mainly due to the diversity of tumor morphology and signal intensity, and the insufficient performance of existing deep learning models in dealing with complex structures and image noise. In order to solve these problems, this paper proposes an automatic tumor segmentation method based on pyramid hierarchical network (PLHN) architecture and DCE-MRI. This method enhances the network's ability to recognize tumor boundaries and details through multi-scale feature extraction and hierarchical processing, and shows stronger robustness when dealing with dynamic changes in DCE-MRI. According to our experimental results on the data set, the method proposed in this paper can effectively improve the accuracy of tumor segmentation, which has strong research and practical significance.

关键词： image segmentation Accuracy Noise Morphology Medical treatment Feature extraction Robustness Integrated circuit modeling Optimization Tumors

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Proximity Estimation with Position Adjustment for Autonomous Industrial Inspection

Proximity Estimation with Position Adjustment for Autonomous...

引用

Artificial Intelligence of Things and systems (AIoTSys), international conference on

作者： Ziyi Zhou Ping Huang Song Zheng Zhezhuang Xu Zixiong Zhuang Education Department of Fujian Province College of Electrical Engineering and Automation Fuzhou University and Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

Due to the similarity in appearance and dense deployment of devices in industrial environments, relying solely on machine vision makes it challenging for inspection robots to accurately identify similar devices. Although wireless signals from industrial internet of things (IoT) can serve as identification features, signal fluctuations impact the accuracy and efficiency of recognition. To address this issue, this paper proposes a proximity estimation algorithm with position adjustment for autonomous industrial inspection. The algorithm considers the spatial relationships between nodes and the topological relationship between the signal strength and variations among the nodes. By analyzing the topological relationship between the signal strength and variations among the nodes, the robot autonomously adjusts its position and selects the proximal node based on the spatial topology relationship between them. We have built an inspection platform using quadruped robots to evaluate the effectiveness of the experiments. The experimental results demonstrate that the algorithm further improves the efficiency of identifying proximal devices while ensuring the estimation accuracy of the algorithm.

关键词： Wireless communication Accuracy Fluctuations Service robots Estimation Inspection Topology Object recognition Quadrupedal robots Industrial internet of Things

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Operation state monitoring method of double column center fracture horizontal rotary disconnector based on laser point cloud 3

Operation state monitoring method of double column center fr...

引用

3rd international conference on Energy internet and Power systems

作者： Lin, Hongdong Li, Fupeng Xie, Xingsheng Jiang, Yuhuan Zhongshan Power Supply Bur Guangdong Power Grid C Zhongshan 528400 Guangdong Peoples R China

A technique utilizing laser point cloud data has been developed for the surveillance of the operational status of a double-column center fracture horizontal rotary disconnector switch. The method includes an action monitoring model for isolating switches to identify and segment point cloud data of isolating switch conducting arm. The model consists of a local feature extraction module, a point cloud feature dimension attention module, a classification module, and a segmentation module. The segmentation module realized the point cloud segmentation function by Interpolate and Unit Pointnet network. The classification module implements the point cloud classification function through a point net layer Pointnet and a fully connected layer (FC). Ultimately, the status of the disconnector's operation is determined by analyzing the differences in the point cloud vectors of the conductive arm between successive frames.

关键词： image segmentation

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The Building Fault Diagnosis Method based on Integrated Least Squares Support Vector Machine

The Building Fault Diagnosis Method Based on Integrated Leas...

引用

internet of Things, Automation and Artificial Intelligence (IoTAAI), international conference on

作者： Guoyuan Quan Jianbin Xiong Xiangjun Dong Yipeng Wu Qi Wang Wulue Zhang Guangdong Polytechnic Normal Universityy Guangzhou China The Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources P.R.China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

This paper proposes an integrated classifier based on the Least Squares Support Vector Machine for fault diagnosis in building electrical systems. Firstly, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method is employed to pre-process the fault signals, thereby enhancing the accuracy and effectiveness of signal analysis. Secondly, Multiscale Sample Entropy is used to extract features. Thirdly, to further improve the classification performance, multiple the Least Squares Support Vector Machine classifiers are integrated to form an integrated Least Squares Support Vector Machine classifier. The experimental results show that the method significantly improves the accuracy and efficiency of fault diagnosis with an accuracy of 98.3% compared to the traditional method. This significantly improves the performance of electrical system fault diagnosis, showing great potential for practical applications.

关键词： Fault diagnosis Support vector machines Accuracy Buildings Noise Reliability theory Feature extraction Entropy internet of Things signal analysis

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Hierarchical DNN-based image Segmentation Algorithm Using Texton, Superpixels, and Layer-Adaptive Loss Functions

Hierarchical DNN-Based Image Segmentation Algorithm Using Te...

引用

international IEEE conference on signal-image Technologies and internet-based System

作者： Cheng-Hsuan Yu Jian-Jiun Ding Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan

image segmentation is critical to object-oriented image processing. Many conventional segmentation algorithms are based on the superpixel, since it integrates the pixels with similar colors and locations in prior and is beneficial for segmentation. Recently, several segmentation algorithms based on deep learning were developed. However, due to the irregular shape and size of superpixels, it is hard to apply the superpixel directly in a leaning-based segmentation algorithm. In this paper, we propose a novel segmentation method that well integrates the techniques of the deep neural network (DNN), the superpixel, adaptive loss functions, and multi-layer feature extraction. First, different from other learning-based algorithm, which applies an image or its bounding boxes as the input, we adopt the mean and the histogram differences of the features of two superpixels as the input of the DNN to determine whether they should be merged. Moreover, to well consider both large- scaled and small-scaled features, a hierarchical architecture is adopted. For different layers, the DNN models with different loss functions are applied. A larger penalty for over-merging is applied in the first layer and a larger penalty for over-segmentation is applied in the following layer. Moreover, according to human perception, the features of colors, areas, the gradient at the boundary, and the texton, which is highly related to the texture, are applied. Experiments show that the proposed method outperforms other state-of-the-art image segmentation methods and produces highly accurate segmentation results.

关键词： Deep learning Training image segmentation Histograms image color analysis Shape Object oriented modeling

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Security of image Using Watermarking Techniques and Visual Cryptography

Security of Image Using Watermarking Techniques and Visual C...

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international conference on Information systems and Computer Networks (ISCON)

作者： Lahar Srivastava Hitendra Garg Department of Computer Engineering & Applications GLA University Mathura

Due to rapid growth in technology there is a need to secure multimedia data like audio, video, text etc. available on the internet. In the proposed work, visual cryptography based watermarking techniques are proposed to secure image data. The watermark is inserted followed by visual cryptography where ‘n’ numbers of shares are created. DCT and IDCT are implemented to insert the watermark. The k shares out of n are required to get the original image where watermark is authenticated. PSNR is used for comparing the original image with images obtained after decryption.

关键词： Visualization image processing Watermarking Streaming media Computer networks Cryptography Discrete cosine transforms

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