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A bioinspired,self-powered,flytrap-based sensor and actuator enabled by voltage triggered hydrogel electrodes

Nano Research 2023年第7期16卷 10198-10205页

作者： Zhiliang Hou Xuebiao Li Xinru Zhang Wendong Zhang Zhong Lin Wang Hulin Zhang College of Information and Computer Taiyuan University of TechnologyTaiyuan 030024China School of Materials Science and Engineering Georgia Institute of TechnologyAtlantaGeorgia 30332USA Beijing Institute of Nanoenergy and Nanosystems Chinese Academy of SciencesBeijing 101400China

Because of its adaptive interfacial property,soft sensors/actuators can be used to perform more delicate tasks than their rigid ***,plant epidermis with a waxy cuticle layer challenges stable and high-fidelity non-invasive electrophysiology since the conventional electrodes are invasive,easily detached from plants,and require complicated setup ***,we report a bioinspired sensor and actuator created by using a conformable electrode interface as an electrical modulation unit on a Venus *** conformable electrode,by employing an adhesive hydrogel layer,can achieve the merits of low impedance,stretchable,biocompatible,reusable,and transparent enough for normal chlorophyll activity to *** to the high sensitivity of a flytrap to a triggering mechanical stimulation,a plant sensor matrix based on flytraps has been demonstrated by capturing the stimulated action potential(AP)signals from upper epidermis,which can orient honeybee colonies by their touch during collecting ***,via frequency-dependent AP modulation,an autonomous on-demand actuation on a flytrap is *** flytrap actuator can be controlled to responsively grasp tiny objects by the modulated signals triggered by a triboelectric nanogenerator(TENG).This work paves a way of developing autonomous plant-based sensors and actuators toward smart agriculture and intelligent robots.

关键词： self-powered hydrogel electrode sensor and actuator flytrap triboelectric nanogenerator

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TCGC: Tweet Classification and Recommendation for Geographical Catastrophes and Events 15th

TCGC: Tweet Classification and Recommendation for Geographic...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Aravind Krishnan, A. Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru India Department of Computer Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031810855

A need for a strategic framework for tweet classification and recommendation has emerged, specifically for geographical catastrophes and events. This paper proposes a semantics-oriented model for disaster tweet classification in which the latent semantic indexing and WikiData are encompassed for topic modelling and auxiliary knowledge addition into the model. Event ontologies highlighting catastrophes also contribute to the information present in the framework. This model encompasses the DT classifier and the transformer classifier in order to classify the dataset and the metadata respectively. The metadata generation also enriches the framework’s knowledge. The framework uses Google’s Knowledge Graph API to generate knowledge graphs and a shuffling agent helps in shuffling the knowledge for diversifying the intermediate solutions. Twitter Semantic Similarity and Adaptive Pointwise Mutual Information (APMI) measures are strong semantics-oriented reasoning models used in the framework for semantic relatedness computation. Animal Migration Optimization which is a metaheuristic optimization framework is also used for the computation of the optimal solution. The paper has a precision of 96.54%, an overall accuracy of 97.02%, and the lowest FDR of 0.04, outperforming all the baseline models considered in this paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge graph

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Improved channel estimation for underwater wireless optical communication OFDM systems by combining deep learning and machine learning models

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Optical and Quantum Electronics 2025年第3期57卷 1-21页

作者： Salama, Wessam M. Aly, Moustafa H. Department of Computer Engineering Faculty of Engineering Pharos University Canal El Mahmoudia Street Beside Green Plaza Complex 21648 Alexandria Egypt OSA Member Department of Electronics and Communications Engineering College of Engineering and Technology Arab Academy for Science Technology and Marine Transport Alexandria1029 Egypt

Recent studies on channel estimation in wireless communication systems have focused on deep learning methods. Our primary contribution is based on the use of DenseNet121 hybrid with Random Forest (RF), Gated Recurrent Units (GRU), Long Short-Term Memory Networks (LSTM), and Recurrent Neural Networks (RNN) to improve the channel estimation and lower the error rate. In order to mitigate inter-symbol interference and map the datasets, this paper introduces M-quadrature amplitude modulation (16-QAM) and orthogonal frequency division multiplexing (OFDM), which is based on quadrature phase shift keying (QPSK). Additionally, the existence or lack of cyclic prefixes forms the basis of our simulation. Additionally, the suggested models are investigated using pilot samples 2, 4, 8, and 64. Labeled OFDM signal samples, where the labels match the signal received after applying OFDM and passing through the medium, are used to train the proposed models. The DenseNet121 functions as a powerful feature extractor to extract intricate spatial information from received signal data. Sequential models like as RNN, LSTM, and GRU are used to model temporal dependencies in the retrieved features. RF is also utilized to exploit non-linear relationships and interactions between features to further increase prediction accuracy and reduce bit error rate (BER). By comparing the models using key metrics like accuracy, bit error rate (BER), and mean squared error (MSE), superior performance is attained based on the DenseNet121_RNN_GRU_RF model. Additionally, the DLMs are assessed against traditional methods like minimal mean square error (MMSE) and least squares (LS). Using the DenseNet121_RNN_GRU_RF model indicates a considerable gain over alternative architectures, with an improvement of 36.3% over DensNet121-RNN-LSTM-RF, according to a comparison of the suggested models without cyclic prefix for OFDM_QPSK. The improvement in percentages of roughly 63.3% over DensNet121-RNN-LSTM, 68.18% over De

关键词： Orthogonal frequency division multiplexing

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Enhancing Crop Yield and Growth Prediction Using IoT-Based Smart Irrigation Systems and Machine Learning Algorithms 2

Enhancing Crop Yield and Growth Prediction Using IoT-Based S...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Shilpa, M. Ravi, P. Sharmila, N. Mallikarjunaswamy, S. Deepak, B.L. Pavithra, G.S. Thazeen, Sadiya Komala, M. Mahendra, H.N. Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Mysore College of Engineering & Management Dept of Computer Science and Engineering Mysuru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Sjc Institute Of Technology Dept of Information Science and Engineering chickballapur India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350372892

This paper introduces 'Smart Irrigation Yield Optimization (SIYO),' a sophisticated IoT-based smart irrigation system aimed at significantly improving crop yield and growth predictions. Traditional irrigation methods such as furrow irrigation, overhead sprinkler systems, and manual watering are analyzed for their inherent inefficiencies, especially in precision and resource management. SIYO overcomes these drawbacks by employing a network of real-time sensors that monitor soil moisture, temperature, humidity, and nutrient levels, thus enabling precise and automated irrigation tailored to the specific needs of crops. This technology not only optimizes water and nutrient usage but also adapts to varying environmental conditions to maximize crop potential. Comparative studies reveal that SIYO enhances water efficiency by 0.30% over furrow irrigation, increases nutrient utilization by 0.25% compared to overhead systems, and boosts the accuracy of crop yield and growth predictions by 0.40% against manual methods. The implementation of SIYO demonstrates the substantial improvements in agricultural productivity and resource sustainability achievable with advanced IoT technologies. © 2024 IEEE.

关键词： Smart agriculture

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis 8

Character Classification and Actor Recognition in Yakshagana...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

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SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification

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IEEE Transactions on Multimedia 2025年 27卷 3251-3265页

作者： Wang, Lei Zhan, Yibing Ma, Leilei Tao, Dapeng Ding, Liang Gong, Chen Nanjing University of Science and Technology Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Jiangsu Nanjing210094 China JD Explore Academy Beijing100000 China Anhui University School of Computer Science and Technology Anhui Heifei230601 China Yunnan University FIST LAB School of Information Science and Engineering Yunnan Kunming650091 China

Recently, Mix-style data augmentation methods (e.g., Mixup and CutMix) have shown promising performance in various visual tasks. However, these methods are primarily designed for single-label images, ignoring the considerable discrepancies between single- and multi-label images, i.e., a multi-label image involves multiple co-occurred categories and fickle object scales. On the other hand, previous multi-label image classification (MLIC) methods tend to design elaborate models, bringing expensive computation. In this article, we introduce a simple but effective augmentation strategy for multi-label image classification, namely SpliceMix. The "splice"in our method is two-fold: 1) Each mixed image is a splice of several downsampled images in the form of a grid, where the semantics of images attending to mixing are blended without object deficiencies for alleviating co-occurred bias;2) We splice mixed images and the original mini-batch to form a new SpliceMixed mini-batch, which allows an image with different scales to contribute to training together. Furthermore, such splice in our SpliceMixed mini-batch enables interactions between mixed images and original regular images. We also provide a simple and non-parametric extension based on consistency learning (SpliceMix-CL) to show the potential of extending our SpliceMix. Extensive experiments on various tasks demonstrate that only using SpliceMix with a baseline model (e.g., ResNet) achieves better performance than state-of-the-art methods. Moreover, the generalizability of our SpliceMix is further validated by the improvements in current MLIC methods when married with our SpliceMix. The code is available at https://***/zuiran/SpliceMix. © 1999-2012 IEEE.

关键词： Image classification

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Classification of Rice Variant using Deep Learning

Classification of Rice Variant using Deep Learning

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Kumar, K Susheel Verma, Prem Kumari Chaurasia, Subham Singh, Nagendra Pratap Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Karnataka Manipal India National Institute of Technology Department of Computer Science and Engineering Punjab Jalandhar India Axis Institute of Technology and Management Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798331533311

Rice is a popular crop in the world. This paper has constructed a model using deep Learning for classification. Classification is a critical process in identifying the class of an image. Here, the Rice dataset has been taken to classify the class of rice Breed. Image Classification is a complex process that is affected by various factors. The emphasis is placed on summarizing essential superior class procedures and the strategies used for improving classification accuracy. This paper uses the deep learning approach for classification, such as CNN, and some activation functions, such as ReLu Softmax with Dense layers. The classification process used various domains, such as Machine Learning and deep learning. In this model, five classes of rice breed have been taken and classified with an accuracy of 9 6%. © 2024 IEEE.

关键词： Adversarial machine learning

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Automatic Target Description File Generation

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Journal of computer science & technology 2023年第6期38卷 1339-1355页

作者：耿洪娜吕方钟茗崔慧敏薛景玲冯晓兵 State Key Laboratory of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Agile hardware design is gaining increasing momentum and bringing new chips in larger quantities to the market ***,it also takes new challenges for compiler developers to retarget existing compilers to these new chips in shorter time than ever ***,retargeting a compiler backend,e.g.,an LLVM backend to a new target,requires compiler developers to write manually a set of target description files(totalling 10300+lines of code(LOC)for RISC-V in LLVM),which is error-prone and *** this paper,we introduce a new approach,Au-tomatic Target Description File Generation(ATG),which accelerates the generation of a compiler backend for a new tar-get by generating its target description files *** a new target,ATG proceeds in two ***,ATG synthesizes a small list of target-specific properties and a list of code-layout templates from the target description files of a set of existing targets with similar instruction set architectures(ISAs).Second,ATG requests compiler developers to fill in the information for each instruction in the new target in tabular form according to the list of target-specific properties syn-thesized and then generates its target description files automatically according to the list of code-layout templates *** first stage can often be reused by different new targets sharing similar *** evaluate ATG using nine RISC-V instruction sets drawn from a total of 1029 instructions in LLVM *** enables compiler developers to gen-erate compiler backends for these ISAs that emit the same assembly code as the existing compiler backends for RISC-V but with significantly less development effort(by specifying each instruction in terms of up to 61 target-specific properties only).

关键词： retargetability compiler target description target backend automatic generator

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Prevention of soil erosion, prediction soil NPK and Moisture for protecting structural deformities in Mining area using fog assisted Smart agriculture system 2

Prevention of soil erosion, prediction soil NPK and Moisture...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Mohanty, Surajit Pani, Subhendu Kumar Tripathy, Niva Rout, Raghunath Acharya, Mousumi Raut, Prakash Kumar Research Scholar Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela India Krupajal Computer Academy Odisha India Cuttack India

A new idea called "smart agriculture" enables farms to be managed using cutting-edge technology like IoT, AI, robotics, drones, etc. The goal is to maximize the use of the resources at hand while producing more crops of higher quality and quantity. Crop output growth is mostly dependent on the fertility and moisture content of the soil. In the past, different fertilizers were added to the soil to boost its nutrients, but this process took time and labour from humans. Subsequently, as technology advanced, the data gathered from IoT devices was processed via cloud computing. Fog computing provides a solution to the significant latency associated with cloud server location that is remote. The main goal of this research is to create a fog-assisted recommendation model that can be applied to the future prediction of soil moisture content and NPK. When combined with agricultural systems, machine learning algorithms provide a proactive, data-driven method for maximizing resource management and soil health. It offers a succinct overview of how to forecast crop growth through the use of hybrid machine learning algorithms on the IFogsim platform to enhance soil quality and irrigation efficiency. Moreover, it lessens soil erosion and increases soil stability. In the mining sector, the suggested technique is helpful for protecting against structural deformities and stabilizing slopes. © 2024 Elsevier B.V.. All rights reserved.

关键词： Fog

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Review of Deep Speech Recognizer using Transcriber 6

Review of Deep Speech Recognizer using Transcriber

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6th IEEE International Conference on Advances in science and technology, ICAST 2023

作者： Tivarekar, Rahulkumar P. Khadye, Ruchita Mahesh Chavande, Saloni Ravindra Talkatkar, Pranita Sachin Mumbai University Finolex Academy of Management and Technology Computer Science and Engineering Department Ratnagiri India

ISBN: (纸本)9798350359817

This research presents the development of a 'Deep Speech Recognizer using Transcriber,' aimed at streamlining the process of capturing and converting live speech into text. The primary problem addressed by this system is the need for efficient and accurate transcription in various contexts, such as meetings, lectures, and interviews. The methodology employed in this project utilizes Jupyter widgets for user interaction, PyAudio for microphone audio capture, Vosk for speech recognition, and recasepunc for punctuation enhancement. Key findings of this research include the successful implementation of a user-friendly system capable of recording and transcribing live speech in real-time. The system exhibits commendable accuracy in speech recognition, making it a valuable tool for professionals and researchers in fields requiring transcription services. Furthermore, the integration of recasepunc significantly improves the readability and usability of the transcribed text. This research contributes to the automation of speech transcription, offering a practical solution to the time-consuming and error-prone process of manual transcription. The system's versatility and ease of use make it a promising tool for enhancing productivity and accessibility in various domains. Future work may involve further optimization and the exploration of additional features to expand the system's capabilities and applications. © 2023 IEEE.

关键词： Speech recognition

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