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Implementation and Analysis of LoRaWAN Network in Landfill

Implementation and Analysis of LoRaWAN Network in Landfill

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International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT)

作者： João Paulo Silvino Belo da Silva Raimundo Carlos Silvério Freire Cleonilson Protásio de Souza Ícaro Modesto Granja Aguiar Post-Graduate Program in Electrical Engineering Federal University of Campina Grande Campina Grande Brasil Department of Electrical Engineering Federal University of Campina Grande Campina Grande Brasil Department of Electrical Engineering Federal University of Paraíba João Pessoa Brasil

ISBN: (数字)9798350350869

ISBN: (纸本)9798350350876

LoRaWAN is a network protocol characterized by low latency, long-range wireless transmission reaching distances of tens of kilometers, and low power consumption of end devices (nodes). These properties favor the application of this technology in various fields related to the Internet of Things, such as environmental monitoring. This article presents methodological strategies for implementing and analyzing a wireless sensor network based on LoRaWAN technology in a landfill to promote future monitoring of system variables. Therefore, a radio station consisting of a LoRaWAN gateway was installed. Physical variables that could influence the proper functioning of the network were considered, such as physical location and specification of the LoRaWAN gateway antenna, obstacles between devices and data transmission parameters. Finally, the analysis of the network performance in the landfill indicated that the most significant issue pertains to the shaded areas resulting from the irregular terrain.

关键词： Wireless communication Performance evaluation Wireless sensor networks Transducers Power demand Transmitting antennas LoRaWAN Landfills Logic gates Soil

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Automatic Heliostat Directing System Based On Microcontroller ESP32

Automatic Heliostat Directing System Based On Microcontrolle...

引用

International Conference on electrical engineering and Informatics, ICEEI

作者： Richard Medyanto Nicholas Sanjaya Suryadiputra Liawatimena Computer Engineering Department Automotive & Robotics Program BINUS ASO School of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta

Solar energy is one of the most abundant sources of renewable energy in Indonesia. Solar energy is now typically harnessed using solar panels, but the low efficiency of photovoltaic cells requires the development of other alternatives. The heliostat is a sunlight directing device with mirrors that can be used in a concentrated solar power system. Current heliostats require high capital investment due to their large frames and expensive components. This research was undertaken to develop a lower cost heliostat using a smaller frame, ESP32 microcontroller, servo motor and low-cost components. The position of the sun can be determined using an algorithm based on the National Oceanic and Atmospheric Administration (NOAA) solar calculator, and the mirror is moved to maintain the sun's reflection on a target. The result of this research is a set of heliostat prototypes consisting of the frame and control system. Tests were carried out to test the performance of the designed heliostat and it was found that the heliostat has an accuracy of about 60 cm and can raise temperatures up to 3.41°C. The conclusion is that the heliostat can be used in a concentrated solar power system to heat boilers in solar power towers.

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Ground Robot Detection for Collaborative UAV-UGV Alignment and Retrieval Operations

Ground Robot Detection for Collaborative UAV-UGV Alignment a...

引用

International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Jamaica Mae L. Pepito Earl Ryan M. Aleluya John Mel A. Bolaybolay Francis Jann A. Alagon Steve E. Clar Jeanette C. Pao Carl John O. Salaan Sherwin A. Guirnaldo Maria Fe P. Bahinting Department of Computer Applications College of Computer Studies Department of Computer Engineering and Mechatronics College of Engineering Department of Electrical Engineering College of Engineering Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

Volcanologists face considerable challenges in ex-ploring and monitoring harsh and dangerous environments like volcanoes. To gather essential data, they use robots, such as drones and mobile robots. However, drones have limited capacity and flight time, while mobile robots face mobility constraints. Combining drones and mobile robots has emerged as an inno-vative technology for long-range monitoring of volcanoes. This approach allows for fast deployment of ground robots using transport drones. However, the retrieval operation of the ground robot is challenging due to inaccurate drone positioning. A visual technique for detecting and aligning the ground robot is crucial during this procedure. In this study, the detection of the ground robot utilizing a drone was investigated. The drone-based detection approach involved (i) capturing ground robot images from various conditions, (ii) training a robust detection model, and (iii) assessing its performance. Furthermore, this study employed the You Only Look Once (YOLOv8) architecture for ground robot detection. Three models were investigated: the plate detection model, the ground robot detection model, and the model for both ground robot and plate detection. The trained ground robot detection model achieved a robust detection performance garnering the precision of 98.9% with a respectable recall of 100%, F1 score of 99.94%, and 98.6% mean average precision, compared to the other two trained models. The trained detection model presented a practical approach, which produced good performance and could predict and segment the ground robot during the alignment procedure for the retrieval operation by the drone.

关键词： Training YOLO Collaboration Predictive models Volcanoes Mobile robots Robots

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FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method

FAD-SAR: A Novel Fishing Activity Detection System via Synth...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Bai, Yanbing Li, Siao Ju, Rui-Yang Yang, Zihao Yu, Jinze Chiang, Jen-Shiun Center for Applied Statistics School of Statistics Renmin University of China Beijing100872 China Graduate Institute of Networking and Multimedia National Taiwan University Taipei City106335 Taiwan Department of Mechanical Systems Engineering Tokyo University of Agriculture and Technology Tokyo183-8538 Japan Department of Electrical and Computer Engineering Tamkang University New Taipei City251301 Taiwan

ISBN: (纸本)9798350389210

Illegal, unreported, and unregulated (IUU) fishing activities seriously affect various aspects of human life. However, traditional methods for detecting and monitoring IUU fishing activities at sea have limitations. Although synthetic aperture radar (SAR) can complement existing vessel detection systems, extracting useful information from SAR images using traditional methods remains a challenge, especially in IUU fishing. This paper proposes a deep learning based fishing activity detection system, which is implemented on the xView3 dataset using six classical object detection models: SSD, RetinaNet, FSAF, FCOS, Faster R-CNN, and Cascade R-CNN. In addition, this work employs different enhancement techniques to improve the performance of the Faster R-CNN model. The experimental results demonstrate that training the Faster R-CNN model using the Online Hard Example Mining (OHEM) strategy increases the Avg-F1 value from 0.212 to 0.216. © 2024 IEEE.

关键词： Deep learning

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On the Performance of Multi - Hop Radio and Optical Wireless Relaying Systems 4

On the Performance of Multi - Hop Radio and Optical Wireless...

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4th West Asian Symposium on Optical and Millimeter-Wave Wireless Communications, WASOWC 2022

作者： Soleimani-Nasab, Ehsan Ghassemlooy, Zabih Graduate University of Advanced Technology Department of Electrical and Computer Engineering Kerman Iran Northumbria University Faculty of Engineering and Environment NewcastleNE1 8ST United Kingdom

ISBN: (纸本)9781665409131

Free space optical (FSO) communications systems give higher information rates in the unlicensed frequency spec-trum bands in contrast to conventional radio frequency (RF) wireless communication systems. A multi-hop serial RF, and FSO system is proposed and the link performance for four different scenarios with amplify-and-forward relaying technique is investigated. Based on the signal-to-noise-ratio statistics, we show closed-form expressions for the bit error rate and ergodic capacity for all scenarios. In addition, we examine the exactness and validity of the derived mathematical expressions using the Monte Carlo simulations. © 2022 IEEE.

关键词： Bit error rate

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Hotel Information System Management Audit Using COBIT 2019 - APO12

Hotel Information System Management Audit Using COBIT 2019 -...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Joy Nashar Utamajaya Suhono Harso Supangat Ford Lumban Gaol Benny Ranti BINUS Graduate Program School of Information System BINUS University Jakarta Indonesia School of Electrical Engineering and Informatic Bandung Institute of Technology Bandung Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company’s business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal.

关键词： Technological innovation Companies Risk management Information technology Interviews Information systems

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Eggplant Leaf Semantic Segmentation in Aerial Imagery for Precision Agriculture

Eggplant Leaf Semantic Segmentation in Aerial Imagery for Pr...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Nouran M. Usman Earl Ryan M. Aleluya Steve E. Clar Francis Jann A. Alagon Carl John O. Salaan Maria Fe P. Bahinting Department of Computer Applications College of Computer Studies Department of Computer Engineering and Mechatronics College of Engineering Department of Electrical Engineering College of Engineering Graduate School of Engineering Mindanao State University-Iligan Institute of Technology Iligan City Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

Eggplant is one of the prominent vegetable crops in the Philippines. Traditional farming practices, like agrochemical spraying, often lack real-time, high-resolution data crucial for informed decisions. Monitoring vegetation indices is vital for crop growth assessment, health monitoring, and precision agriculture. However, optimizing agrochemical spraying for specific crop needs remains a challenge. Unmanned aerial vehicles (UAV s) offer a transformative solution. RGB cameras, designed for visual imagery, may not provide accurate vegetation index measurements. computer vision, through leaf detection, plays a crucial role in assessing crop health. This study aims to distinguish eggplant leaves from their surroundings. Subsequently, the authors compared two open-source segmentation models, namely U-Net and FPN. Two pre-trained encoders, VGG16 and VGG19, were used in both models to further evaluate their performance. Using a drone-mounted RGB camera, the authors gathered images of mature eggplant leaves. The experimental findings demonstrate the effectiveness of the VGGl6-based FPN architecture for real-time leaf segmentation tasks as it acquires 97.24 % and 96.73 % accuracy with a latency time of 31.5 ms and 32.6 ms, on 0.5 and 0.75 threshold values. The study recommends FPN with VGG16 for its accurate and low-latency performance in time-sensitive leaf segmentation applications.

关键词： Precision agriculture Accuracy Semantic segmentation Crops Vegetation mapping Spraying Autonomous aerial vehicles

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A Comparison of Keras Application Models with Pre-Trained Weights in Predicting the Disease of Lanzones Leaf 14

A Comparison of Keras Application Models with Pre-Trained We...

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14th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

作者： Muhali, Adzmer Linsangan, Noel Graduate School Mapua University Intramuros Manila Philippines Department of Computer Science Information Technology and Engineering Sulu State College Sulu Jolo Philippines School of Electrical Electronics and Computer Engineering Mapua University Intramuros Manila Philippines

ISBN: (纸本)9781665464932

Much research has already been done to detect diseases on plant leaves using different architectures of the Tensorflow Keras Application. Despite this, no existing or ongoing research on the disease on Lanzone's leaf has been conducted. Lanzones also provide essential health benefits to people and the market demands. This study helps researchers choose the Keras Application's exemplary architecture for detecting the disease on Lanzones Leaf. Hence this study compares four (4) architectures of Keras Application that provide impressive results in other plant disease classification such as VGG16, VGG19, ResNet101, and ResNet152V2 to determine the best model for detecting the Lanzones Leaf disease. In this study, the models saw the healthy leaf and diseases such as Algal Leaf Spot and Leaf Blight Spot of Lanzones Tree. The methods used pre-processing and augmentation on the collected dataset, as well as training the models for more epochs, to improve the models' performance and learning process. The analysis and evaluation results show that VGG16 is the best model among those models utilized in detecting diseases of lanzones leaves;hence, the VGG16 achieved 89.09% of overall accuracy. © 2022 IEEE.

关键词： Plants (botany)

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A universal infrared absorption analysis system using deep learning

A universal infrared absorption analysis system using deep l...

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2024 Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024

作者： Nakgawa, Eito Sasaki, Hiroto Nishijima, Yoshiaki Department of Electrical and Computer Engineering Graduate School of Engineering Japan Yokohama National University 79-5 Tokiwadai Hodogaya-ku Japan Institute of Advanced Sciences Yokohama National University 79-5 Tokiwadai Hodogaya-ku Japan Institute for Multidisciplinary Sciences Yokohama National University 79-5 Tokiwadai Hodogaya-ku Japan

We focused on reducing the experimental cost of obtaining mid-infrared absorption spectra using machine learning techniques. The large amount of spectral data from HITRAN and quantum scientific calculations can be a u... 详细信息

关键词： Adversarial machine learning

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Classification of Lanzones Tree Leaf Diseases Using Image Processing Technology and a Convolutional Neural Network (CNN) 4

Classification of Lanzones Tree Leaf Diseases Using Image Pr...

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4th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2022

作者： Muhali, Adzmer S. Linsangan, Noel B. Graduate School Mapua University Intramuros Manila Philippines Information Technology and Engineering Sulu State College Department of Computer Science Sulu Jolo Philippines School of Electrical Electronics and Computer Engineering Mapua University Intramuros Manila Philippines

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Nowadays, many artificial intelligence systems are developed to detect and classify plant leaf diseases via image classification, and those researches achieved high-performance results. However, those Artificial Intelligence Systems categorize various leaf diseases, but none precisely for Lanzones. Briefly, no existing or current research has been studied to detect and classify leaf diseases of Lanzones tree in the early stages using an Artificial Intelligence System. This paper presents a device for categorizing the healthy and with diseases (Algal Leaf Spot and Leaf Blight Spot) on the leaves of the Lanzones tree thru image classification using deep learning. It is limited to the device's development and implementation in the target locale. The methods applied include data pre-processing and augmentation techniques to the created CNN model. This will be integrated into a system using the Raspberry Pi 4 Model B and the Tensorflow Lite Interpreter. The performance analysis and evaluation of the system were based on the confusion matrix, which shows how the system successfully classified the healthy and disease of Lanzones leaves with an accuracy rate of 80%. © 2022 IEEE.

关键词： Neural network models

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