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Fruit Defect Inspection System Using Image processing and IoT Framework 5

Fruit Defect Inspection System Using Image Processing and Io...

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5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Chaudhari, Bhushan Patil, Durwa Patil, Hemant Patil, Rupal Gawali, Jui SVKM's Institute of Technology Department of Information Technology Dhule India

ISBN: (纸本)9781665475242

the consumption of fruits is in high demand due to their nutritional value. Most of the fruits available in the market are processed through chemical practices hampering their quality. Exposure to fruit preservatives and carbides enhances its life and ripens it faster. However, eating such fruit results in poor health and increases the probability of getting infected by various threatening diseases such as cancer, tuberculosis, etc. Organic farming is practiced in some areas of India to achieve fruit quality, but its inadequate to fulfill the demand. To overcome the issues mentioned, a model based on IoT is proposed in this research. A system to separate quality fruits from a basket is presented in this article. the classification will be done using a deep learning technology, Convolutional Neural Network (CNN) which uses a database consisting of pictures of three fruits particularly, apples, oranges, and bananas are used in the experiment. the input from the alcohol (MQ3) sensor and methane (MQ4) sensor are fed forward to node MCU. the input in turn is provided to Arduino UNO for comparison with preprocessed audit set. the inception V3 algorithm is used for classification purposes. this research proposes a cost-effective and near-to-accurate solution to issues in automated fruit quality identification. © 2023 IEEE.

关键词： Internet of things

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Text Matching Model with Multi-granularity Term Alignment 5

Text Matching Model with Multi-granularity Term Alignment

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5th IEEE international conference on Electronics Technology, ICET 2022

作者： Yang, Ning Shao, Yabin Li, Zhen Chongqing University of Posts and Telecommunications Key Laboratory of Computational Intelligence College of Computer Science and Technology Chongqing China

ISBN: (纸本)9781665485081

Text matching is one of the fundamental research tasks in the field of natural language processing. It can be applied to a large number of NLP tasks, such as information retrieval, question, and answer systems and repetition. In this paper, we propose a-matching model with multi-granularity term alignment (MGTA). the model extracts word information at different granularities through convolutional neural networks and enhances the model effect by aligning the original location features at different word granularities, enabling the model to obtain multiple granularities of information during matching. We conduct experiments on the Q&A dataset, the-implication dataset, and the paraphrase recognition dataset, respectively, and compare them with current mainstream models in terms of accuracy, MAP and MRR evaluation metrics, and has fewer parameters, which greatly improves the inference speed. © 2022 IEEE.

关键词： Convolutional neural networks

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Real-time Face Structure Reproduction Model for Generative Adversarial networks 5

Real-time Face Structure Reproduction Model for Generative A...

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5th IEEE international conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Yang, Fan Ju, Fang-Chen Zhang, Qiao-Xi Han, Meng Ma, Fu-Min Nanjing University of Finance and Economics College of Information Engineering Nanjing China

ISBN: (纸本)9781665499132

In this paper, the image to image translation algorithm based on generative countermeasure network is used to capture and extract the face features in the source image and target image, obtain the mapping function after training the target image model, and transfer the features of the original face image to the face model of the target image. the conditional generative adversarial networks is selected as the solution to the face to face mapping problem. At the same time, a novel method for real-time face reconstruction of single target video sequence is proposed. When studying the selected loss function, we find that the system has wide applicability and strong ease of use, and there is no need to adjust the parameters. the results show that in addition to face capture and feature migration, in many tasks that need to process highly structured images, the pix2pix algorithm based on cGAN is also a highly expected method. this means that this system can not only be more perfect in face feature migration, but also have the possibility to expand the function in other fields of image processing. © 2022 IEEE.

关键词： Generative adversarial networks

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Hardware Accelerator for Deep Neural networks on Edge Devices Based on RISC-V Open Standard Instruction Set Architecture

Hardware Accelerator for Deep Neural Networks on Edge Device...

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2024 IEEE Region 10 conference, TENCON 2024

作者： Ting Ng, Yen Tumian, Afidalina Ho, Eric Tatt Wei Universiti Teknologi Petronas Department of Electrical & Electronic Engineering Perak Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798350350821

Recent advances in transformer neural network architecture are constrained by their substantial computational demands, which pose significant challenges in edge computing environments. In these limited network connectivity settings, local data processing on resource-limited embedded processors becomes necessary. We designed a hardware custom function unit (CFU) to accelerate neural network computation in edge computing environments. Our circuit functions as a co-processor to an embedded processor and implements new custom instructions compatible with the Reduced Instruction Set Computer 5th Generation (RISC- V) Open Standard Instruction Set Architecture (ISA). We implemented a systolic array of programmable processing elements (PE) with local cache to offload computation of dense matrix multiplication from the embedded processor. through simulation, we achieved upto threefold improvement in processing speed for matrix multiply-and-accumulate operation of transformer models with three encoder layers and 256 dimensions. Our design provides a pathway to seamlessly integrate hardware acceleration to enhance inference computation in embedded processors by leveraging on the advantages of open standard ISA. With defined interfaces to a RISC- V processor core, our hardware can be imported as an IP block in programmable or application specific design flows. On the software side, dense matrix multiplications instructions are replaced with our custom instructions while the rest of the instruction compilation and assembly steps can continue to leverage on open-source software tools that convert Python ONNX models to RISC-V instructions. © 2024 IEEE.

关键词： Open source software

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DynaGraph: Dynamic Graph Neural networks at Sc 5

DynaGraph: Dynamic Graph Neural Networks at Sc

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5th ACM SIGMOD Joint international Workshop on Graph Data Management Experiences and Systems and Network Data Analytics, GRADES-NDA 2022, co-located with ACM SIGMOD conference

作者： Guan, Mingyu Iyer, Anand Padmanabha Kim, Taesoo Georgia Institute of Technology Atlanta United States Microsoft Research Redmond United States

ISBN: (纸本)9781450393843

In this paper, we present DynaGraph, a system that supports dynamic Graph Neural networks (GNNs) efficiently. Based on the observation that existing proposals for dynamic GNN architectures combine techniques for structural and temporal information encoding independently, DynaGraph proposes novel techniques that enable cross optimizations across these tasks. It uses cached message passing and timestep fusion to significantly reduce the overhead associated with dynamic GNN processing. It further proposes a simple distributed data-parallel dynamic graph processing strategy that enables scalable dynamic GNN computation. Our evaluation of DynaGraph on a variety of dynamic GNN architectures and use cases shows a speedup of up to 2.7X compared to existing state-of-the-art frameworks. © 2022 Owner/Author.

关键词： Graph neural networks

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MLNLP 2022 - 2022 5th international conference on Machine Learning and Natural Language processing, conference Proceedings

MLNLP 2022 - 2022 5th International Conference on Machine Le...

引用

5th international conference on Machine Learning and Natural Language processing, MLNLP 2022

ISBN: (纸本)9781450399067

the proceedings contain 66 papers. the topics discussed include: answering questions over tables based on TAPAS and graph attention model;prompt-based few-shot learning for table-based fact verification;human finger motion analysis based on machine learning;relevance calculation of users’ document sending behavior under the network social platform;dense attention memory network for multi-modal emotion recognition;a faster method for generating Chinese text summaries-combining extractive summarization and abstractive summarization;pedestrian trajectory prediction based on improved social spatio-temporal graph convolution neural network;long short-term dynamic graph neural networks: for short-term intense rainfall forecasting;an effective time-aware encoder for temporal knowledge graph reasoning;and keyword extractor for contrastive learning of unsupervised sentence embedding.

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Evaluating Attention in Convolutional Neural networks for Blended Images 5

Evaluating Attention in Convolutional Neural Networks for Bl...

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5th IEEE international conference on Image processing Applications and Systems (IPAS)

作者： Portscher, Andrea Stabinger, Sebastian Rodriguez-Sanchez, Antonio Univ Innsbruck Dept Comp Sci Innsbruck Austria

ISBN: (纸本)9781665462198

examine how attention mechanisms in the human brain work. they are particularly suited for this research area, as a subject needs to focus on particular features in an image to be able to classify superimposed objects. As Convolutional Neural networks (CNNs) take some inspiration from the mammalian visual system - such as the hierarchical structure where different levels of abstraction are processed on different network layers we examine how CNNs perform on this task. More specifically, we evaluate the performance of four popular CNN architectures (ResNet18, ResNet50, CORnet-Z, and Inception V3) on the classification of objects in blended images. Since humans can rather easily solve this task by applying object-based attention, we also augment all architectures with a multi-headed self-attention mechanism to examine its effect on performance. Lastly, we analyse if there is a correlation between the similarity of a network architecture's structure to the human visual system and its ability to correctly classify objects in blended images. Our findings showed that adding a self-attention mechanism reliably increases the similarity to the V4 area of the human ventral stream, an area where attention has a large influence on the processing of visual stimuli.

关键词： image classification blended images biological inspiration

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FaultBit: Generic and Efficient Wireless Fault Detection Using the Internet of things 20th

FaultBit: Generic and Efficient Wireless Fault Detection Usi...

引用

20th EAI international conference on Mobile and Ubiquitous Systems - Computing Networking and Services (EAI MobiQuitous)

作者： Dolui, Koustabh thangarajan, Ashok Samraj Morshchavka, Sergii Liu, Zhaoyi Michiels, Sam Hughes, Danny Katholieke Univ Leuven Dept Comp Sci Imec Distrinet Leuven Belgium

ISBN: (纸本)9783031639883;9783031639890

the timely monitoring and maintenance of industrial machines is critical to prevent expensive disruptions and down-time. the Internet of things (IoT) offers a solution to instrument production processes at lower cost and complexity. However, wireless IoT networks are bandwidth constrained, which precludes the transmission of high frequency signals such as vibration or electrical current. Prior approaches to tackling this problem require a high degree of application-specific reengineering. In this paper, we argue for a new approach to fault detection that is accurate, efficient and applicable to a large class of fault detection problems. We propose FaultBit, an application independent toolkit for fault classification that can gather, compress and classify data using IoT networks. We evaluate FaultBit in two representative scenarios using current and vibration data. In both cases, FaultBit offers classification accuracy 99%, which is very close to application-specific classifiers, while requiring 512x less bandwidth, enabling a battery life of several years.

关键词： Internet of things Wireless sensor networks Machine Learning Signal processing Energy awareness

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Computational Techniques High-Quality Wireless Communication to the Outside World for Further processing in Wireless sensor networks

Computational Techniques High-Quality Wireless Communication...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE international conference on

作者： C. Jisha Chandra R. Nagarajan K.R.N. Kalis S. Kannadhasan Department of Electrical and Electronics Engineering Gnanamani College of Technology Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy Tamilnadu India Department of Electronics and Communication Engineering Study World College of Engineeirng Coimbatore Tamilnadu India

Due to important advancements in hardware manufacturing technology and the creation of effective software algorithms, a wireless sensor network is a system made up of a lot of small, inexpensive sensors that interact wirelessly. this system is now technically and financially practical. the employment of sensor networks in mission-critical environments like combat zones as well as in more commonplace security and business applications like building and traffic surveillance, habitat monitoring, and smart homes, among others, has a lot of promise. Wireless sensor networks, however, provide unique security challenges. Because WSNs are used for so many security-sensitive applications, security is now the top issue for protocol designers who are working on WSNs. We have made an attempt to create a complete list of all known security weaknesses in wireless sensor networks in this study, which also explores a broad range of WSN attacks and their categorization techniques. both the difficulties encountered and the many security methods that may be used to solve them. We tackled the problem in this study and put up a complete, integrated security that would provide security services for all sensor network tasks. It is anticipated to be utilised extensively in the future because of the wireless connectivity, processing power, and sensor technologies it incorporates. A number of security vulnerabilities are present with wireless communication systems.

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Performance Comparison of Ovarian Tumour Classification Using Deep Neural Network 5

Performance Comparison of Ovarian Tumour Classification Usin...

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5th IEEE international conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Srilatha, K. Chitra, P. Sumathi, M. Jayasudha, F.V. Sanju, I. Mary Sajin Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665493604

Ovarian tumour originates in ovaries posing a serious threat to women. As a result, it leads to abnormal cells which have the ability to spread to the other regions of the body. Ovarian tumour is a sort of risky improvement that impacts ovaries in females, and is hard to perceive at early the phase because of which it stays as one of the guideline wellsprings of illness end. the ultrasound imaging is the low cost and best to detect tumour in ovaries. To improve tumour detection accuracy in ultrasound ovarian images, enhanced preprocessing, image segmentation, feature extraction and feature selection, and classification method are proposed. the enhanced preprocessing, tumour segmentation, feature extraction and feature selection, and tumour classification methods have been analyzed to segment and classify the tumour effectively in ultrasound ovarian tumour images. the Anisotropic Diffusion Filter (ADF) has been applied for the preprocessing of the ultrasound ovarian image to improve the image quality for further processing such as segmentation, feature extraction and feature selection, and classification. the Improved Whale Search Optimization (IWSO) segmentation method has been applied to segment tumour in the ultrasound image efficiently. the Deep Neural Network (DNN) method has given more accuracy in classifying the ultrasound ovarian tumour image into benign or malignant. the DNN has high sensitivity, high specificity, high accuracy, high PPV, high NPV, low FPR, low FNR, low FDR, low time complexity, low error rate, and low false classification ratio during the tumour classification compared with the existing methods. © 2023 IEEE.

关键词： Deep neural networks

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