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Relay coil-based long-range RFID reader/writer for ISO15693 identification 4

Relay coil-based long-range RFID reader/writer for ISO15693 ...

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4th International conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2024

作者： Yuan, Xinyu Zhou, Haiyan Chen, Siren Bai, Yurun Wu, Jialing Lu, Liling Chen, Jiayi Institute of Information and Communication Guilin University of Electronic Technology China

ISBN: (数字)9781510679887

ISBN: (纸本)9781510679870

Radio Frequency Identification (RFID) is at the key of Internet of Things development. As RFID technology continues to mature, its application scenarios are expanding, opening a new chapter in the Internet of Things' evolution. Addressing the limitations of current RFID systems, which struggle to simultaneously meet the range and complexity requirements for long-range RFID identification in challenging environments, a system was developed. Through practical experiments, our system can transmit and receive high-frequency signals using RFID antennas at distances of up to 0.9 meters. It is capable of reading information from tags and allows for visual operations through a host computer, effectively meeting real-world production needs. © 2024 SPIE.

关键词： Field programmable gate arrays (FPGA)

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DigiSketch: Automating the Conversion of Hand-Drawn Diagrams to Digital

DigiSketch: Automating the Conversion of Hand-Drawn Diagrams...

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2024 International conference on communication, Computer Sciences and Engineering, IC3SE 2024

作者： Kamboj, Deepika Dipanjali Mittal, Archi Tiwari, Anupam Agarwal, Divya Department Of Information Technology Ghaziabad India

ISBN: (纸本)9798350366846

Hand-drawn diagrams play a crucial role in various fields, from education to engineering, yet their integration into the digital realm remains a time consuming and error prone process. This research paper introduces an innovative approach to automatically digitize hand-drawn diagrams, leveraging advancements in computer vision, machine learning, and image processing techniques. Our approach begins with acquiring high-resolution scans or images of hand-drawn diagrams, performing a series of preprocessing steps, enhancing image quality, and correcting distortions to ensure the most accurate representation using annotation tools to annotate diagrams. Further, this work employed TrOCR for text recognition, ensuring precise transcription of handwritten labels. The extracted components are vectorized into scalable and editable digital formats. This approach not only saves time but also preserves the nuances of hand-drawn diagrams, making it understandable to educators and engineers. The automatic digitization of hand-drawn diagrams presented in this paper bridges the gap between analog and digital format, simplifying the transition and enhancing the usability of these valuable visual representations in modern age. © 2024 IEEE.

关键词： Character recognition

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HAZOP entity-relation ternary recognition model based on deep learning 4

HAZOP entity-relation ternary recognition model based on dee...

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4th International conference on communication Technology and information Technology, ICCTIT 2024

作者： Li, Zhuang Lu, Duhui Gao, Dong Zhang, Beike Li, Dazi Beijing University of Chemical Technology College of Information Science and Technology Beijing China China Special Equipment Inspection & Research Institute Beijing China

ISBN: (纸本)9798331528973

Industries such as chemical and manufacturing often have significant safety risks associated with their production processes, and it is critical to conduct a HAZOP safety analysis of the entire process before an accident occurs. However, the large amount of expert knowledge contained in HAZOP reports is not fully utilized. To solve the current problems, this paper proposes a fused deep learning model to extract unstructured expert knowledge of HAZOP by entity recognition and relation extraction of HAZOP safety analysis text. Compared with ordinary text, HAZOP text has a denser professional vocabulary, which leads to great difficulty in entity recognition. Based on the language feature of the HAZOP report, this paper combines the ACmix model, which is good at processing local features in the visual field, with the SPN model to effectively improve the recognition ability of the HAZOP entity-relation triplet. By changing the size of the convolution kernel and reducing the dimension of the attention mechanism, the ACmix model is applied to the text domain for the first time. The research results in this paper can be used in the future for the automatic construction of knowledge graphs in the fields of chemical industry and manufacturing, which can help enterprises analyze safety accidents as well as optimize and improve equipment. © 2024 IEEE.

关键词： Chemical industry

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Inventory Control in Food Industry Using an AI-Driven Precision Counting 13

Inventory Control in Food Industry Using an AI-Driven Precis...

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13th IEEE Global conference on Consumer Electronic, GCCE 2024

作者： Aharari, Ari Mehdipour, Farhad Sojo University Faculty of Computer and Information Sciences Kumamoto Japan Otago Polytechnic Department of Information Technology Auckland International Campus Auckland New Zealand

ISBN: (纸本)9798350355079

In the food industry, manual counting, due to its laborious and error-prone nature, not only consumes time but also exposes the industry to significant losses. Introducing an automated counting system for the food industry alleviates errors and amplifies overall efficiency. This paper discusses the development of an AI-based system for visual counting food products. Utilizing deep learning algorithms, including YOLO (You Only Look Once), the system analyzes digital images of products to generate precise counts. We have implemented YOLOv8 and compared its performance in product detection against its predecessor, YOLOv7. The transition from YOLOv7 to YOLOv8 demonstrates substantial improvements in both accuracy and execution time. Comparative analysis revealed that YOLOv8 processes images 6.3 times faster than its predecessor, marking a significant leap in processing speed. © 2024 IEEE.

关键词： Food industry

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A Hybrid Natural Language processing (HNLP) for Model Transformation 5

A Hybrid Natural Language Processing (HNLP) for Model Transf...

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5th International conference on Electronics and Sustainable communication Systems, ICESC 2024

作者： Madhavi, Banka Chaithanya, T. Rama Babu, S. Giri Pace Institute of Technology Sciences Department of Computer Science & Engineering AP Ongole India Pace Institute of Technology Sciences Department of Computer Science & Engineering AP Ongole India

ISBN: (纸本)9798350379945

Model-to-model transformations involve converting one type of the model into another. This can be used in many ways for various reasons, considering model performance with particular deployment platforms. This paper introduces a Hybrid Natural Language processing (HNLP) model to visualize model-to-model (M2M) transformation. The proposed HNLP combines Sub-word Embedding (FastText), Encoder-Decoder Architecture, and Attention-based Seq2Seq. The proposed approach focused on processing standard text into meaningful information. Subword Embeddings with FastText retain grammatical aspects by demonstrating words as n-grams, which enhances the interpretation of out-of-vocabulary phrases and unusual word structures. The Encoder-Decoder Architecture translates input sequences into symbolic representations that can be used for various NLP tasks. The Attention-based Seq2Seq method extends this approach by constantly focusing on vital regions of the input sequence during decoding, improving the model's capacity to provide accurate and contextually appropriate outputs. By combining these techniques, the HNLP model enhances sequence transformation quality and gives a more interpretable and visual path for understanding how input transformations occur between models. This visualization capacity is crucial for debugging, model improvement, and understanding the inner workings of complicated NLP systems. Experimental results show that the HNLP model performs well in various NLP tasks, demonstrating its promise as a reliable tool for practical applications and theoretical investigations into model-to-model transformations. © 2024 IEEE.

关键词： Decoding

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Study on the difference of brain visual information processing between Parkinson's disease patients with hallucinations and healthy people based on EEG 3

Study on the difference of brain visual information processi...

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3rd International conference on Computer Science and communication Technology, ICCSCT 2022

作者： Qu, Liangcheng Liu, Chuan Yin, Kuiyin Zhang, Li Shen, Bo Nanjing Research Institute of Electronic Techology Jiangsu Najing China Department of Geriatrics Affiliated Brain Hospital of Nanjing Medical University Jiangsu Najing China

ISBN: (纸本)9781510661240

Computer vision is a mode task that uses computer to learn from training data and apply the learned experience to specific task. When patient experiences hallucinations, brain will call the memory stored in the hippocampus and process through specific pattern, and brain visual terminal will receive the image that does not exist in the real world. Patients with Parkinson's disease (PD) for more than 10 years are usually accompanied by hallucinations, which leads to changes in visual information processing and information perception pathway in brain. Electroencephalogram (EEG) is a spontaneous electrophysiological signal of the brain, which can reflect the working mechanism of the brain. In order to explore the changes of brain information perception pathway in PD, we recruited 5 PD patients with hallucinations, 5 healthy elderly and 5 healthy young subjects to participate visual classification experiment. The EEG collected in the experiment was analyzed by spectrum and brain network. The analysis found that the Delta, theta and alpha power of frontal lobe and occipital lobe in PD patients increased, the beta and gamma power decreased, and the brain fell into the internal circulation, the interaction between brain regions decreased. The lack of interaction may be one of the main causes of hallucinations. © 2022 SPIE.

关键词： Brain

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Enhancing Taiwan Guava Grading through Advanced Image processing and Deep Learning Techniques 2

Enhancing Taiwan Guava Grading through Advanced Image Proces...

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2nd International conference on Advances in Computational Intelligence and communication, ICACIC 2023

作者： Yeshwanth, Jashvinu Sweetlin, J Dhalia MIT Campus - Anna University Department of Information Technology India

ISBN: (纸本)9798350318456

Grading unique fruits like Taiwan guavas has historically relied heavily on specialized human examination of visual and textural characteristics. This dependence on inconsistent manual inspection incurs massive inefficiencies across guava harvesting, sorting, sales and processing operations. The lack of rapid, precise and standardized grading practices results in improper quality evaluation, revenue losses for farmers and inadequate transparency for consumers regarding produce freshness and safety. This pioneering research aims to overcome these challenges by developing an advanced computer vision system tailored to automate the visual grading process for Taiwan guavas. A large and diverse dataset of guava images representing natural variations in shape, size, color, texture and defects is systematically compiled. This dataset is leveraged to optimize a state-of-the-art deep neural network model using techniques like transfer learning and data augmentation. Comprehensive benchmarking experiments demonstrate that the optimized model achieves 89% test accuracy in classifying guavas into industry-standard grades based on quality parameters learned from visual cues. The model operates with high precision and recall, with minimal confusion between adjacent grades. The proposed computer vision-based solution can potentially bring immense benefits in efficiency, consistency, transparency, sustainability and value across guava harvesting, sorting, logistics and sales. Automated grading ensures only premium quality guavas reach consumers, thereby improving farmer incomes and customer satisfaction. © 2023 IEEE.

关键词： Object detection

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BERT-Based Natural Language processing System for Online Social information Retrieval on Illness Tracking

BERT-Based Natural Language Processing System for Online Soc...

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2023 International conference on Advances in Computation, communication and information Technology, ICAICCIT 2023

作者： Chowdhary, Mohit Nandal, Poonam Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Haryana Faridabad India

ISBN: (纸本)9798350344387

With the growing rise of digital platforms such as facebook, twitter and others that attract billions of people around the world, it has gained a lot of traction as a source of unstructured data. The volume of unstructured data is expected to expand at a 60 percent annual rate in the future years. The emergence of social media has facilitated the expansion of unstructured data. Thanks to social media, everyone may now express and share their thoughts and opinions on a wide range of topics. Infodemiology is one such popular and rapidly expanding information retrieval method (i.e., information epidemiology). As per the concept, a set of approaches that study data, particularly health data on the internet, for the purpose of public health studies and policy. Web data is analyzed to detect disease outbreaks faster than traditional surveillance in infoveillance (also known as information surveillance). This research has created a prototype software model for extracting spatiotemporal diseases from diverse data sources in this vein. To identify prospective sickness instances, the proposed method gathered tweets and other postings and used NLP semantic algorithms with transformers called BERT. The prototype system is tuned to improve disease detection using rules given by dissimilar semantic similarity algorithms. The prototype's output is a visual representation of likely disease forms and spread, which is a straight IE produce from Twitter and other sites. © 2023 IEEE.

关键词： information Retrieval Natural Language processing Online Social Network

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The Application of Event-Triggered-Based Model Predictive Control for Trajectory Tracking of Quadrotor in Municipal Engineering 15th

The Application of Event-Triggered-Based Model Predictive Co...

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15th International conference on Computer Science and its Applications, CSA 2023 and 17th KIPS International conference on Ubiquitous information Technologies and Applications, CUTE 2023

作者： Ma, Haochen Shao, Bin Wei, Tianxu Zhao, Jingbo School of Information and Control Engineering Qingdao University of Technology Qingdao China Qingdao University of Technology Qingdao266520 China

ISBN: (纸本)9789819724468

The maintenance of municipal facilities has always been an important public affairs, in order to ensure the smooth traffic and driving safety of the city, it is necessary to carry out regular inspection of the road surface and traffic signs. The use of drone inspection can find problems such as uneven road surface and illegal parking, so as to avoid traffic congestion and accidents. In this paper, a control method based on the combination of model predictive control and event triggering is proposed to control the trajectory tracking of the four-rotor UAV. The control method of model predictive control (MPC) enables the four-rotor UAV to track the preset path well. In addition, the original four-rotor UAV dynamic model is simplified, and the event-triggered control idea designed in this paper solves the problem of excessive consumption of communication resources in model predictive control well, and provides conditions for installing functions such as visual processing and infrared remote sensing on the UAV in subsequent municipal maintenance. The simulation results show that the proposed control scheme can better track the reference trajectory of the quadrotor and reduce the consumption of communication resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Trajectories

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Scene Image Classification Based on Optimized Transfer Learning Using ResNet 4

Scene Image Classification Based on Optimized Transfer Learn...

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4th International conference on Multimedia processing, communication and information Technology, MPCIT 2024

作者： Shrinivasa, S.R. Aishwarya, P.S. Linus, Kipatnui Kuvempu Univesity Dept of Computer Science Karnataka Bhadravathi India ATRIA Institue of Technology Dept. of Information Science & Engg Bangalore577451 India Kuvempu University Dept of Computer Sceince Shankaraghatta Shimoga577451 India

ISBN: (纸本)9798350375466

Scene image classification is a process that involves automatically identifying the type of environment depicted in an image or video, such as a street, office, mountain, etc. This process is accomplished by analyzing various visual features of the image, including shapes, colors and textures. Machine learning (ML) algorithms are then utilized to classify the image into different categories. However, deep CNNs can be challenging due to the requirement of large-scale datasets, which may not always be available for small-scale datasets. To address this problem, Transfer Learning can be applied to fine-tune pre-trained models for scene image classification. Conventional methods that use handcrafted aspects have limited representation ability, whereas deep CNN model features contain additional semantic and structural details. Therefore, the proposed approach employs an integrated with five pre-trained convolutional neural networks (CNNs) and is fine-tuned by a ResNet50-based transfer learning model that utilizes interlayer discriminating features for scene image classification. The suggested model's effectiveness, accuracy and efficiency are evaluated on a small-scale scene dataset. Comparing with the various current approaches, our findings perform with high accuracy. Therefore, the proposed approach can be considered as an effective and efficient model for scene image classification, especially for small-scale datasets. The carried-out experimentations with complex context scene datasets, including the UC Merced Land Dataset (UCM), the UIUC sports event dataset, and the MIT scene dataset. The experimental results show that the suggested approach is superior to current research on scene image classification approaches. © 2024 IEEE.

关键词： Adversarial machine learning

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