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Development of smart flood monitoring and early warning system using weather forecasting data and wireless sensor networks-A review 3

Development of smart flood monitoring and early warning syst...

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3rd International conference on intelligent Communication Technologies and Virtual Mobile networks, ICICV 2021

作者： Subashini, M.J. Sudarmani, R. Gobika, S. Varshini, R. School of Engineering Avinashilingam Institute for Home Science and Higher Education for Women Department of ECE Coimbatore India

ISBN: (纸本)9780738111834

Flooding could be a normal marvel and it is an overflow of water that submerges the land area, which is ordinarily dry in nature bv creating a negative effect on the society due to its tall misfortune on human lives. Due to the negative imnact on the residents and coastal areas, flood prediction and alerting nlavs an important role in the current generation. Recently, a severe flooding took place in the Indian state Kerala during the last monsoon season with a heavy unusual rain, which has led to a severe loss and not happened previously during the century. Within the fortnight, nearly the died people strength is 373, where almost 83% of the dams in the state are opened out to avoid further loss of lives. Thus, the chances of flood occurring should be periodically monitored. Hence, an early warning system is required to save the lives of the people in the endangered areas by using Wireless sensor Network fWSN). In this paper, a system of Wireless sensor networks has been developed and it comprises of sensor hubs that are conveyed at the particular surge and helpless areas for performing a real-time surge checking and discovery. Wireless sensor networks GWSN1 can be deployed for many other real time projects, as the data could be collected rapidly and efficiently. The sensor node consists of certain sensors such as temperature sensor, ultrasonic and rain sensor with limited processing and communication capabilities. The detected information from the sensors is transmitted to the microprocessor for further preparation. The processed information is transmitted to cloud by using a LoRA (Long Range) module. A mobile application has been developed to provide an early warning to the people in the affected areas, where mobile is very handy and conveys information as soon as possible. Hence, the latest technology like sensor network will be used to determine the accurate level of flooding and also the mobile phone connected with this technology will send the forecasting info

关键词： Internet of things

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Low-Cost Domain Adaptive Experience Based Localization for Autonomous Robots 23

Low-Cost Domain Adaptive Experience Based Localization for A...

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6th International conference on Advances in Robotics, AIR 2023

作者： Kumar, Utkarsh Kala, Rahul Nandi, Gora Chand Indian Institute of Information Technology Allahabad India

ISBN: (纸本)9781450399807

Localization is one of the key aspect of any automated navigation system, it means finding own position in a given environment, without it an agent might get lost. In a dynamic environment localization is a very difficult task because various factors such as weather and illumination may change over time. We developed a low-cost solution to the localization problem, relying only on a camera sensor, without using expensive sensors like lidars. The solution allows to handle dynamic nature of the environment and uses previous knowledge for localization, hence it is named as Experience Based Localization. STREET-CNN is a core part of the method which is trained on STREET dataset. This dataset contains several images of streets in IIIT-A along with their GPS coordinates as labels. The trained network calculates the similarity score between the two cross domain images. Presented method is equipped with Kalman Filter to include speed variations of agent while predicting its location. Results produced with domain adaptation technique is compared with, without the domain adaptation technique. © 2023 ACM.

关键词： Deep neural networks

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Hydra Radio Access Network (H-RAN): Accurate Estimation of Reflection Configurations (RCs) for Reconfigurable intelligent Surfaces (RIS)

Hydra Radio Access Network (H-RAN): Accurate Estimation of R...

引用

International conference on information and Communication Technology Convergence (ICTC)

作者： Rafid I. Abd Kwang Soon Kim Junxiu Han Scho. of Electrical and Electronic Eng. Yonsei University Seoul South Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

The necessity for a cohesive network that seamlessly integrates various networks, technologies, and services is becoming increasingly pressing. By adopting innovative networking solutions that unify these disparate networks, Hydra radio access networks (H-RANs) embody a forward-looking approach that is essential for navigating the complexities of the evolving digital landscape. By integrating advanced communication and sensing capabilities, as well as harnessing the power of artificial intelligence/machine learning (AI/ML), H-RANs are set to redefine how services are delivered and managed within modern telecommunications networks. This comprehensive strategy ensures that these networks are not only current but also poised for future innovations. Concurrently, reconfigurable intelligent surfaces (RISs) are envisioned as programmable surface structures that control electromagnetic wave reflection, thereby modifying the wireless communication environment. Despite their advantages, a major challenge for RIS technology lies in the high overhead associated with beam alignment and channel estimation for MMW systems. These conventional methods require extensive pilot signaling to estimate channel state information (CSI) accurately, leading to significant computational and operational burdens. This study proposes a novel approach to accurately estimate RIS's reflection configurations (RCs) using real-time sensor data extracted from sensor and radio units (SRUs) and AI/ML D-engine. This method aims to enhance RIS functionality by providing precise information that allows dynamic adjustments to RCs. Simulations demonstrate that our approach significantly boosts network performance, achieving high selection accuracy by achieving a remarkable 95% selection accuracy using only five RC index candidates.

关键词： Training Technological innovation Accuracy Wireless networks Simulation Channel estimation Reconfigurable intelligent surfaces Surface structures sensors Radio access networks

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Research on Passive Location of Maneuvering Target Based on IMM-SRCKF Algorithm 8th

Research on Passive Location of Maneuvering Target Based on ...

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8th International conference on Signal and information processing, Network and Computers, ICSINC 2021

作者： Dong, Chunyun Guo, Zhi Chen, Xiaolong Yang, Xu Yang, Yang Xi’an Key Laboratory of Intelligent Instrument and Packaging Test Xidian University Shaanxi Xi’an710071 China State Key Laboratory of Astronautic Dynamics Shaanxi Xi’an710043 China

ISBN: (纸本)9789811933868

To address the problem of three-dimensional non-cooperative maneuvering target tracking in the air, a multi-sensor cooperative passive positioning solution based on the Kalman filter framework is proposed. Firstly, the constant velocity (CV) model and the Singer model are involved to approximate the unknown kinetic characteristic of the non-cooperative target. Secondly, the network positioning is carried out by multiple passive sensors deployed in different areas on the ground, and a centralized observation equation is established. Then, the registration error of each sensor is extended to the target state vector considering its influence on the positioning accuracy, and a unified state space model is built up. Finally, the interacting multiple model (IMM) algorithm based on the square root cubature Kalman filter (SRCKF) filter is devised to realize the estimation of the target state as well as the registration error. Numerical simulations indicate that the presented algorithm can achieve the joint estimation of the target state and the sensor registration error in real time with high accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Kalman filters

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An Online Machine Learning-based Content Caching Scheme in Mobile Edge Computing networks 15

An Online Machine Learning-based Content Caching Scheme in M...

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conference on sensors and Systems for Space Applications XV

作者： Zhao, Qi Li, Yi Liu, Hang DeCortec, Nicholas Tucker, Frank Chen, Genshe Intelligent Fus Technol Inc Germantown MD 20874 USA Catholic Univ Amer Washington DC 20064 USA US Army Combat Capabil Dev Orlando FL USA

ISBN: (纸本)9781510651197;9781510651180

Mobile Edge Computing (MEC) is an emerging and fast-growing distributed computing paradigm. It brings the computation and storage resources closer to mobile users while also processing data at the network edge to improve response time and save bandwidth. In tactical virtual training environments, latency is a key factor that affects training performance. Additionally, MEC provides both information service environment and cloud computing capabilities to enable real-time virtual training. Therefore, we designed a machine learning-based data caching and processing scheme for the virtual training networks. The design consists of three tiers, mobile devices, edge servers, and cloud servers, respectively. By pre-caching the critical content objects close to the mobile devices, our MEC network enables data transmission and processing at low latency. Utilizing machine learning techniques, our caching scheme can predict and select the content objects to be cached with optimal storage efficiency at network edge servers. Specifically, we decoupled the content caching problem into two subproblems, namely probability learning and content selection. For probability learning, the edge servers estimate the probability and frequency that each content object will be requested in the near future. The estimate is according to the content request pattern learned over time. For the content selection, the edge servers determine the content objects for caching to minimize the expected content delay with limited storage. To evaluate the performance of our proposed scheme, we developed a testbed with real mobile devices and servers. The experimental results validated the feasibility and significant performance gains of the proposed scheme.

关键词： Content caching latency reduction machine learning mobile edge computing

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IoT Based Smart Automated Poultry Farm Management System 10

IoT Based Smart Automated Poultry Farm Management System

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10th International conference on Emerging Trends in Engineering and Technology - Signal and information processing, ICETET-SIP-22 2022

作者： Ramteke, Bharati Dongre, Snehlata G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9781665467414

According to research there are an estimated 850 million poultry birds in India and the number of farmers involved in the business is estimated at 30 million. This clearly means that the poultry farm is one of the most important and healthy sources of income in India. But it does require a lot of effort to run a poultry farm, as it requires general bird control, health monitoring, Food, dihydrogen monoxide, and local hygiene. The actual process of all of this is more challenging and more difficult. Therefore in order to acquire the owners of the poultry farm. Proposed intelligent Automated Poultry Management System using IoT. The program includes feeding and watering birds using sensors used inside containers. The system will set an alarm when the stock element and dihydrogen monoxide are down. The lights in the Poultry farm is control by a sensor. Automatic food distribution, pure supply of dihydrogen monoxide, egg accumulation can be done through this system. © 2022 IEEE.

关键词： information processing Humidity Containers Birds Market research Temperature control Monitoring

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Identity Re recognition Technology Based on Improved Lightweight ConvNetx Model 42

Identity Re recognition Technology Based on Improved Lightwe...

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42nd Chinese Control conference, CCC 2023

作者： Siting, Peng Lei, Li Zhu, Hongbo Jun, Ren Zhichao, Song Yang, Jiabing The Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beihang University School of Electronics and Information Engineering Beijing China

ISBN: (纸本)9789887581543

In the engineering field, there is a requirement for re recognition, including the identity recognition of cooperative and non-cooperative targets, that is, different sensors shoot targets in the same scene, and the identity recognition of targets from two perspectives is required, also known as target accurate retrieval. This method is widely used in fields such as automatic driving and military strikes. In the actual application scenario, deploying the re-recognition algorithm on the embedded processor needs to ensure the real-time computing speed and accuracy, so the complexity of the deep learning algorithm must be reduced, and pruning and lightweight processing are required. In the engineering field, when the image acquired by the sensor is subject to natural interference such as cloud and fog weather or fire and smoke, the difficulty of re-recognition increases. This paper adopts the lightweight improvement of ConvNeXt network model, and fine-tuned it on the basis of its original feature extraction function, so that it can successfully migrate to infrared image classification and also complete the extraction of infrared image target detail distinguishing features, and at the same time introduce the alignment of similar targets in infrared cross-view recognition. In the original similarity calculation, the average value is directly obtained from the spatial dimension of the output feature layer, which is replaced by the weight of the position in each space for estimation. The feature information of the multi-view marine ship target is extracted from the multi-sensor marine image, and its similarity is calculated, so as to determine the identity, thus reducing the support conditions and reconnaissance costs, and improving the accurate detection and tracking ability under the diverse dynamic reconnaissance conditions. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Feature extraction

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Optimizing Environmental Parameters in Smart Greenhouse Design with IoT and sensor networks

Optimizing Environmental Parameters in Smart Greenhouse Desi...

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Computing and Data Science (ICCDS), International conference on

作者： Edralin R. Raro Thelma D. Palaoag College of Agriculture and Natural Resources Camarines Norte State College Philippines College of Information Technology and Computer Science University of the Cordilleras Baguio City Philippines

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

This paper explores contemporary agricultural practices and cutting-edge technologies to improve crop yields. Implementing an intelligent greenhouse model that automates farm tasks, minimizing manual intervention. The closed structure of the greenhouse shields plants from adverse weather conditions such as temperature extremes, humidity fluctuations, and variations in air and soil moisture. The study introduces an innovative automatic irrigation system to address the shortcomings of traditional agricultural irrigation methods, emphasizing efficient water consumption control and energy utilization. The proposed system integrates a distributed wireless sensor network, including soil moisture, humidity, and temperature sensors, utilizing the Blynk cloud-based IoT platform and ThinkSpeak, Soil moisture and temperature sensors feed data to NodeMCU V3 ESP8266 WiFi transceiver module controller, A key feature is integrating Android applications and web servers, enabling remote control and monitoring. This technological innovation ensures control over water and energy resources and changes climates, thereby creating sustainable and technologically driven agrarian practices.

关键词： Temperature sensors Irrigation Wireless sensor networks Green products Soil moisture Humidity Transforms

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Compression Design for Efficient information Sharing in a Network of Radio Frequency sensors

Compression Design for Efficient Information Sharing in a Ne...

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European Signal processing conference (EUSIPCO)

作者： Fraser K. Coutts John Thompson Bernard Mulgrew Institute for Imaging Data and Communications University of Edinburgh Edinburgh UK

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

There is a desire to maximise efficiency when sharing positioning, navigation, and timing (PNT) information captured by low size, weight, power, and cost (SWAP-C) radio frequency (RF) sensors in distributed sensor networks. Such information can be used to position the sensors or RF sources in both civilian and military applications. We propose a novel framework for improving efficiency when sharing PNT information in a network of sensors receiving radar waveforms in an urban, contested electromagnetic environment. We show that informed, at-edge compression strategies that prioritise the transmission of PNT information in the presence of interferers can be obtained using information theory, and that these techniques outperform the uninformed strategies typically utilised during compressed sensing. Furthermore, we exhibit the flexibility of these strategies when facilitating trade-offs between different types of PNT information. We demonstrate the impact of compression level and signal-to-noise ratio on PNT activities and we confirm that, for a given network bandwidth, sensing gains can be achieved by combining compressed measurements from multiple sensors.

关键词： Radio frequency Simulation Electromagnetic scattering Estimation Distributed databases Radar sensors Timing Electromagnetics information theory

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Multi-Classification of Ultra-Wideband Non-Line-of-Sight Ranging Signals using Deep Learning

Multi-Classification of Ultra-Wideband Non-Line-of-Sight Ran...

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intelligent Control, Measurement and Signal processing and intelligent Oil Field (ICMSP), International conference on

作者： Mengyao Dong Yiming Liu Yifang Zhang School of Information Science and Technology Southwest Jiaotong University Chengdu China

Ultra-wideband (UWB) is an emerging technology with numerous applications, such as indoor positioning, object tracking, and wireless sensor networks. However, non-line-of-sight (NLOS) scenarios can cause significant performance degradation. In this paper, we propose a deep learning model specifically designed for accurately identifying various types NLOS UWB ranging signals for better error mitigation. We extract 13 feature parameters from low-cost UWB sensors to characterize channel quality. Our proposed deep learning model consists of a convolutional neural network module, a long short-term memory module, and a fully connected module for extracting spatial and time-domain features. Through field experiments, we demonstrate that our model achieves high accuracy and F1 score of 0.98 and 0.97, respectively, indicating its potential for accurately identifying different types of NLOS cases in UWB ranging.

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