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Smart and Safe Conveyor Belt System for Bottling Plants Using IoT

Smart and Safe Conveyor Belt System for Bottling Plants Usin...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International conference on

作者： R. Parimala K Srinivasan K P Sreejith Kumaran Department of Electronics and Instrumentation Engineering Sri Ramakrishna Engineering College Vattamalaipalayam Coimbatore Tamilnadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

In modern industrial environments, automation is critical in enhancing efficiency and safety. This paper proposes an innovative approach to controlling industrial conveyors by integrating a Raspberry Pi with a digital image processing system capable of real-time human detection. Consequences aside, the principal objective is to cease conveyor operation immediately upon detecting an approaching individual to prioritize individuals’ safety and prevent catastrophes. In 2021, a thorough examination of conveyor accidents across seven discrete heavy industries reaffirmed the imperative nature of implementing such precautions. The system employs the OpenCV module, which comprises object detection algorithms and potentially deep learning models such as YOLO or Faster R-CNN, to discern individuals via digital image processing methods and the Raspberry Pi’s computational capabilities. Audible and visual feedback devices provide information on the conveyor’s status, and failsafes are incorporated to halt its motion promptly in the event of a malfunction. This endeavor aims to enhance industrial safety by integrating software and physical components. The importance of automation in fostering safer work environments and mitigating potential hazards is underscored.

关键词： YOLO Visualization Automation Digital images Image processing Standards organizations Real-time systems

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Study of motion delay correction algorithms in time synchronization

Study of motion delay correction algorithms in time synchron...

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2024 International conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Liu, Xiaoyi Li, Jiayin Hu, Xiaoyang Ladimirovich, Perliuk Vladimir Shao, Yunzhuo College of Equipment Engineering Shenyang Ligong University Liaoning Province Shenyang110159 China Institute of Aerospace Instruments and Systems St. Petersburg University of Aerospace Instrumentation St. Petersburg190000 Russia Inner Mongolia First Machinery Group Co Ltd. Inner Mongolia Autonomous Region Baotou014030 China

ISBN: (纸本)9781510689176

As human exploration and research of science extends from the ground to deep space, for satellites with faster relative motion speeds and time information that is difficult to accurately obtain the time, this paper proposes a POLA motion delay correction algorithm, which only needs the motion speed of one of the two parties of time synchronization and the position information of both parties to correct the motion delay difference, so that the two-way time synchronization accuracy is not affected by the speed accuracy of the poorer party The algorithm only needs the velocity and position information of one of the time synchronization parties to correct the delay difference, so that the two-way time synchronization accuracy is not affected by the velocity error of the party with poor velocity accuracy. The effectiveness of the algorithm is verified through simulation tests. © 2025 SPIE.

关键词： Clocks Satellites Motion measurement Ranging Antennas Motion analysis Signal processing Aerospace engineering Kinematics Receivers

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ECG pulse train analysis

ECG pulse train analysis

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2021 International conference on INnovations in Intelligent systems and Applications, INISTA 2021

作者： Topalova, Hristina Alexiev, Kiril Bulgarian Academy of Sciences Institute of Information and Communication Technologies Sofia Bulgaria

ISBN: (纸本)9781665436038

The electrocardiogram reflects the electrical activity of the heart. It is used for heart state estimation and diagnosis of a number of heart diseases. Recently new ECG devices have appeared measuring the electrical activity of the heart with unprecedented accuracy. These data determine the need to create new methods for their processing. This article presents a package of algorithms aimed at a more detailed analysis of ECG data obtained with increased sampling rate and higher accuracy. © 2021 IEEE.

关键词： Electrocardiograms

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An Online Approach to Balancing the Energy Consumption for IoT Sensors in UAV-based MEC systems

An Online Approach to Balancing the Energy Consumption for I...

引用

International conference on Wireless Communications and Signal processing (WCSP)

作者： Yang Li Xiaohui Lin Suzhi Bi Mingjun Dai Gongchao Su College of Electronics and Information Engineering Shenzhen University Shenzhen China School of Computer Science Kashi University Kashi China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In this paper, we consider an unmanned aerial vehicle-enabled (UAV-enabled) Mobile Edge Computing (MEC) Internet of Things (IoT) system consisting of multiple energy harvesting devices. In the system, the UAV acts as the edge computing platform providing computation service to the devices. We assume that the energy and sensing data arrive at the devices stochastically. As the amount of energy harvested by devices can differ significantly, we attempt to balance the energy consumption among devices thus avoiding the loss of devices due to the depletion of the battery energy. To ensure energy fairness, we employ an a-utility function, which incorporates the device's remaining energy and energy expenditure. Without any future knowledge of the data and energy arrivals, our objective is to design an online algorithm to jointly optimize the long-term system throughput and utility, while satisfying the stability requirement of the data queue. We formulate the problem as multi-stage stochastic programming that involves functions of time averages and propose an online algorithm based on the perturbed Lyapunov optimization technique. Due to the time-coupling of different variables, we first transform the original problem into a multi-stage stochastic optimization problem involving only time averages. Then, we utilize the Lyapunov optimization method to decouple the transformed problem into a series of per-slot deterministic subproblems. In each slot, we alternatively optimize the auxiliary variables, the data sensing rate, the computation offloading, the communication resource allocation, and the trajectory of the UAV. Simulation results show that the online algorithm can maintain the stability of the data queue. Additionally, we observe a tradeoff between the system's lifetime and throughput when adjusting the parameter $\alpha$ , as well as a tradeoff between throughput and delay for the Lyapunov method.

关键词： Energy consumption Simulation Signal processing algorithms Transforms Throughput Autonomous aerial vehicles Stability analysis Sensors Delays Internet of Things

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A Machine Learning Approach for Real Time Prediction of Last Minute Medical Appointments No-shows 14

A Machine Learning Approach for Real Time Prediction of Last...

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14th International Joint conference on Biomedical Engineering systems and Technologies (BIOSTEC) / 14th Int Conf on Bio-inspired systems and Signal processing (BIOSIGNALS) / 14th Int Conf on Biomedical Electronics and devices (BIOdevices)

作者： Almeida, Ricardo Silva, Nuno Andre Vasconcelos, Andre Inst Super Tecn INESC ID Lisbon Portugal Learning Hlth Grp Luz Saude Lisbon Portugal

ISBN: (纸本)9789897584909

A no-show is when a patient misses a previously scheduled appointment. No-shows cause an impact in the healthcare sector, decreasing efficiency. When a patient misses an appointment the clinic resource are wasted, postpones his or her chance to get treated for a medical condition and denies medical service to another patient. In this research, machine learning techniques are used to find patterns in healthcare data and make no-show predictions. A no-show prediction model is proposed to integrate machine learning techniques into a model that supports the testing of predictions on different datasets. The model is integrated into an online medical appointment booking platform to allow the models and predictions made, to be saved and integrated into a real-time system. Machine learning techniques are tested using three datasets with different characteristics. Through these tests, the model proposed can find the best features, which are similar in every dataset. The results obtained are compared to other prediction algorithms and techniques.

关键词： No-show Healthcare Prediction algorithms Pre-processing Machine Learning Techniques

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An Efficient Beam-Sharing Algorithm for RIS-aided Simultaneous Wireless information and Power Transfer Applications

An Efficient Beam-Sharing Algorithm for RIS-aided Simultaneo...

引用

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

作者： Nguyen Minh Tran Muhammad Miftahul Amri Je Hyeon Park Dong In Kim Kae Won Choi Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

Simultaneous wireless information and power transfer (SWIPT) is a key technology for enabling future high-tech lifestyles by guaranteeing the perpetual operation of trillions of low-power IoT devices. Currently, reconfigurable intelligent surface (RIS) is a promising technology for achieving cost- effective and energy-efficient wireless technologies. In this paper, we propose an efficient beam-sharing algorithm for RIS-aided SWIPT systems. The proposed algorithm maximizes the power transferred from the power transmitter (Ptx) to the power user (PU) while minimizing that to the data user (DU) but maximizing the signal from the data transmitter (Dtx) to DU. Simulation results demonstrate that the proposed beam-sharing algorithm effectively delivers power from Ptx to PU while limiting the power sent by Ptx to DU but maximizing the received signal from Dtx.

关键词： Wireless communication Limiting Transmitters Simulation Signal processing algorithms Optimization methods Reflection coefficient

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Automated Identity Document Recognition and Classification (AIDRAC)-A Review

Automated Identity Document Recognition and Classification (...

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2022 International conference on Augmented Intelligence and Sustainable systems, ICAISS 2022

作者： Vijaya Shetty, S. Madhumitha, R. Reddy, Mekala Meghana Shettar, Shreya Murthy, Tejashree Krishna Nitte Meenakshi Institute of Technology Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9781665489621

In this technological era, official documents like Government approved IDs, Certificates, and other documents are paper-based or image based. The problem arises when an organization has to go through each of these documents manually and extract, enter or search for information. Automated Identity Document Recognition and Classification (AIDRAC) is one of the techniques to tackle this problem by decreasing manual effort. This kind of model automatically classifies government-issued identity documents into predefined categories such as AADHAR card, PAN card, and Driving License. It also recognizes and extracts details from the documents uploaded, providing the users the option to autofill their details upon scanning. This can be achieved through the application of Image processing algorithms using Machine Learning. The extracted information can be stored succeeding relevant access and permissions. The data can be viewed by authenticated consumers when required. Due to the automation of manual work, the implementation of this model saves time, thus saving resources. This paper presents an overview of the exhaustive survey of the techniques employed to build such models and, a comparative analysis of these techniques. It has been found from the survey that there are no models existing that completely accomplish the said automation. Our future research is to build an automation model that can be used in various organizations like Banks, Education Institutions, Law firms, Travel Agencies etc. to autofill the details extracted from the scanned images of Identity proofs. The Sequence diagram of the proposed system is also presented in this paper. The proposed system is expected to outperform the existing models. © 2022 IEEE.

关键词： optical character recognition

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RenalCare: Personalized Alerting System for Renal Cancer Recurrence Prevention via IoT and ML

RenalCare: Personalized Alerting System for Renal Cancer Rec...

引用

International conference on Communications and Signal processing

作者： P Gajalakshmi J Raja Department of CSE Adhiparasakthi Engineering College Affilated to Anna University Melmaruvathur India Department of ECE Adhiparasakthi Engineering College Affilated to Anna University Melmaruvathur India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The early detection of renal cancer recurrence plays a critical role in improving patient outcomes and quality of life. In this study, we propose a novel approach for an Early Warning System for Renal Cancer Recurrence leveraging wearable IoT devices, smartphone integration, cloud-based data processing, and advanced machine learning and deep learning algorithms. Specifically, we employ a range of wearable sensors, including pulse oximeters, blood pressure monitors, temperature sensors, and activity trackers, to capture relevant physiological parameters associated with renal cancer recurrence. Optional implantable sensors, such as glucose monitors and biomarker sensors, are also considered for enhanced monitoring capabilities. Smartphone integration serves as a central hub for data aggregation, enabling real-time monitoring, alerts, and feedback mechanisms for both patients and healthcare providers through mobile applications. Sensor data is securely transmitted to cloud-based platforms for centralized storage and processing, where data fusion techniques are implemented to integrate sensor data with electronic health records (EHR), imaging data, and other clinical information for comprehensive analysis. Machine learning and deep learning algorithms are developed and trained on integrated datasets to detect patterns indicative of renal cancer recurrence, analyzing multi-modal data sources to identify subtle indicators of recurrence. Finally, a real-time alerting system is implemented to trigger notifications to healthcare providers when abnormal patterns or deviations from baseline are detected, enabling timely intervention and proactive management strategies. This comprehensive Early Warning System enables continuous monitoring, early detection of abnormalities, and personalized interventions, ultimately leading to improved patient outcomes and quality of care in the management of renal cancer recurrence.

关键词： Temperature sensors Deep learning Temperature measurement Signal processing algorithms Sensor fusion Real-time systems Internet of Things

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A hybrid Chinese named entity recognition method for Internet of Things

A hybrid Chinese named entity recognition method for Interne...

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2022 International conference on algorithms, Microchips and Network Applications

作者： Wang, Ying Wang, Zehao Li, Hong Liu, Peipei Li, Yachao Zuo, Fang Institute of Intelligence Networks System Henan University Henan475001 China Henan Experimental Teaching Demonstration Centre of Modern Network Technology Henan University Henan475001 China Intelligent Data Processing Engineering Research Center of Henan Province Henan University Henan475001 China Institute of Information Engineering Chinese Academy of Sciences Beijing China Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Henan475001 China

ISBN: (纸本)9781510653290

In order to facilitate government departments to assess security risks and prevent infiltration, it's necessary to recognize the IoT device from open data by the method of Named entity recognition (NER). In this study, a hybrid method of chinese NER which contains neural networks and templates is proposed to extract IoT device information from open web. The model first initiates characters of input through BERT, which can create character embedding while reduce dependency with external datasets. Then, the initiated vectors are fed into a multi-layers BiLSTM model to encode the contextual representation of each character, and a linear CRF layer after BiLSTM is used to assign the scores to every character for entity annotation. In addition, a dictionary and rule base is constructed based on the characteristics of IoT devices to correct the annotation results. We experimented the methods on the dataset of IoT, the results prove the proposed model achieves better recognition effect than other models, and the F-scores value is 88.9%. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Internet of things

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Study on Opto-Mechatronic Hybrid Integration Technology Based on 2.5D Advanced Packaging

Study on Opto-Mechatronic Hybrid Integration Technology Base...

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International conference on (ICEPT) Electronic Packaging Technology

作者： Congying Yan Yong Ruan Jiao Teng Yu Wu Yuankai Zhou Fulei Hou Wenjun Hou Materials Science and Engineering School University of Science and Technology Beijing Beijing China Department of Precision Instruments Tsinghua University Beijing China Qi Yuan Laboratory Beijing China MEMS Institute of Zibo National High-Tech Industrial Development Zone Zibo China Department of Electronic Information Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350353808

ISBN: (纸本)9798350353815

Based on the development trends of micro-electro-mechanical systems (MEMS) and micro-optoelectronic systems, in this paper, a hybrid integration technology for opto-mechatronic micro systems based on 2.5D advanced packaging technology is proposed for the integration of optoelectronic detectors, MEMS sensors, and processing chips. Utilizing a collaborative design approach, a hybrid integration interposer based on Through-Silicon Via (TSV) technology was designed, with TSV diameter/depth of $10 \ \mu \mathrm{m}/100 \ \mu \mathrm{m}$ , Redistribution Layer (RDL) line width of $25 \ \mu \mathrm{m}$ , and optical well window of $600\times 900 \ \mu \mathrm{m}$ . The interposer integrates high-aspect ratio TSV technology and large-size optical well window etching technology compatibly. This hybrid integration technology with wire-free interconnections methods reduces the traditional wire-bond interconnection resistance, improves the signal quality of optoelectronic detectors and conditioning chips, and reduces parasitic effects in optoelectronic systems. The hybrid integration process and design technology of opto-mechatronic microsystems developed based on TSV technology have reduced the system volume from $50\text{mm}\times 50\text{mm}\times 10\text{mm}$ to $11\text{mm}\times 11\text{mm}\times 2.45\text{mm}$ while ensuring high performance. This technology is laying the foundation for the development of 2.5D/3D opto-mechatronic hybrid integration based on interposers.

关键词： Micromechanical devices Integrated optics optical interconnections Packaging optical detectors Silicon Etching

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