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检索条件"任意字段=Intelligent Sensors, Sensor Networks and Information Processing Conference"

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AI Driven Rat Detection for Warehouse

AI Driven Rat Detection for Warehouse

引用

Emerging Smart Computing and Informatics (ESCI), conference on

作者： Ann Vincent Bhavesh Patil Aditya Murbade Hemalata Mote Shruti Mehata Dept. Elec and Telecomm. Engg. Don Bosco Institute of Technology Mumbai Mumbai India BharatGodam Solutions LLP Ahmedabad India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The project’s goal is to create a system for controlling insect activity and monitoring of warehouses in sizable grain storage facilities. The idea behind the system uses neural networks, deep learning algorithms, and imagery processing methods to identify, monitor, track, and notify users of the activity of pests, notably rats, in real-time. By using algorithms that use deep learning for real-time object recognition and authentication, rats may be accurately identified. In addition, the sensors augment optical detection by using non-visual methods to detect motion and activity, providing full surveillance regardless of regions that are completely concealed. The system’s network of carefully positioned imaging devices and sensors allows for constant, continuous surveillance of the warehouse ambiance. The system activates and displays an indication once it recognises a rat, and it logs the precise time and place of the event. To ensure prompt action and response, warehouse administrators get updates in real time via email, SMS, or a mobile app. Through the application of artificial intelligence (AI), this initiative seeks to improve the security of food, reduce monetary damage, and revolutionize pest control at storage facilities for grain.

关键词： Deep learning Tracking Surveillance Neural networks Real-time systems sensor systems Optical sensors Object recognition Artificial intelligence intelligent sensors

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Advanced Flood Management and Rescue Strategies to Securing Lives from Flood Risks

Advanced Flood Management and Rescue Strategies to Securing ...

引用

International conference on ent Computing and Control Systems (ICICCS)

作者： Manoranjitham E Kavin V Prasand S Praveen Kumar B Saravanan R Department of Electronics and Telecommunication Karpagam College of Engineering Coimbatore Tamilnadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Both the locating of flood victims and the provision of continuous alarm information to the Disaster Rescue Force are accomplished by the Flood alarm and Rescue System. Additionally, it shows where the individuals who are trapped in the water are. Afterwards, a flood alarm system might be activated to notify the public. Contrarily, in an emergency, the Rescue System will direct the rescue workers to the people who need them. The individuals in need will activate the rescue operation by pressing the button that the government has given them. The "rescue team will use the signal processing device to receive and decode the encoded communication signal that it emits". Because of this, they will be able to find the individual far more quickly than was previously achievable with the technology at their disposal. This is the case since the top goals of the rescuing force are determined by our system. Our prototype is built with the idea that individuals may simply incorporate the gadget into their daily tasks when floods are in the area. The inclusion of a torch in our product ensures its continued viability and makes it suitable for usage in low-light situations, such as during floods.

关键词： Temperature sensors Wireless sensor networks Force Prototypes Pressing Signal processing Control systems Real-time systems Floods Testing

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IoT Based Water Management and Monitoring System

IoT Based Water Management and Monitoring System

引用

intelligent Data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Muthulakshimi S Malathy Sathyamoorthy Akash S Charumathi P Sri Suvathi N M Department of Information Technology KPR Institute Of Engineering And Technology Coimbatore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

IoT (Internet of Things) has revolutionized various industries, and water management is no exception. This abstract introduces an IoT-based water management system designed to address the challenges of water scarcity, pollution, and inefficient distribution. Leveraging IoT devices like sensors, actuators, and communication modules, the system creates a networked infrastructure for real-time monitoring and management of water resources. sensors deployed throughout the water distribution network collect data on flow, pressure, quality, and consumption patterns, which is then transmitted to a central server or cloud platform. The system instantly detects pipe leakage and promptly halts the water flow, even in the absence of human presence. This automated response helps prevent water scarcity. The system supports remote monitoring and control via web-based dashboards or mobile applications, facilitating proactive maintenance and troubleshooting. Additionally, it provides data analytics and reporting functionalities, empowering stakeholders to make informed decisions and promote water conservation initiatives. Overall, this IoT-based water management system offers a comprehensive solution to enhance sustainability, efficiency, and resilience in water distribution networks.

关键词： Water conservation Water pollution sensor systems Real-time systems Maintenance Internet of Things Stakeholders Sustainable development Water resources intelligent sensors

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Enhanced Security-Aware Beamforming and Trajectory Design for UAV-Assisted Integrated Sensing and Communication

Enhanced Security-Aware Beamforming and Trajectory Design fo...

引用

sensors and information Technology (ICSI), International conference on

作者： Zeyu Yang School of Information Engineering Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Integrated Sensing and Communication (ISAC) en-ables simultaneous sensing and communication functions and is regarded as a cutting-edge technology for future mobile networks. In this paper, we propose a UAV-enabled joint sensing and communication (ISAC) system, where the UAV serves as a base station. In the presence of eavesdroppers, the system aims to ensure that multiple sensing targets are effectively illuminated while maximizing the sum of achievable secrecy rates for multiple communication users under secure communication constraints. To achieve this, we develop a solution based on alternating optimization (AO) and successive convex approximation (SCA). Simulation results validate the effectiveness of the proposed method, demonstrating its ability to balance communication and sensing requirements while significantly enhancing communication security.

关键词： Array signal processing Simulation Radar Physical layer security Integrated sensing and communication Autonomous aerial vehicles Vectors Security Trajectory optimization sensor arrays

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I-KAN: Reconstructing Over-Range Inertial Signals

I-KAN: Reconstructing Over-Range Inertial Signals

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

作者： Yifeng Wang Shu Zhang Yi Zhao Shenzhen Graduate School Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Inertial sensors are widely used in modern society, covering automotive, aerospace, robotics, and consumer electronics. However, these widely used sensors suffer from range limitations, causing signal saturation and information loss under high-dynamic motion conditions. To address this problem, we introduce the Kolmogorov-Arnold networks (KAN) into inertial sensor signal processing for the first time and propose the I-KAN model. Unlike traditional deep learning models that treat signals as discrete point sequences, KAN treats the signal as a continuous curve through embedded spline functions, which allows it to infer the lost signal segments by exploring the continuity and fluctuation patterns of signal curves. Moreover, considering deep learning models suffer from the hallucination problem that generates chaotic and spurious outputs, we propose the Generation Hallucination Entropy (GHE), which quantifies and reduces hallucinations by enhancing the consistency of outputs for similar inputs, thus improving the stability and reliability of the model. Given the absence of research and dataset for reconstructing inertial over-range signals, we release the first inertial over-range signal recovery dataset (IOSRD) in Github, which consists of inertial sensor data from 10 smartphones. The results demonstrate that I-KAN effectively handles varying degrees of signal saturation and sets a benchmark in inertial over-range signal reconstruction.

关键词： Deep learning Fluctuations Inertial sensors Benchmark testing Stability analysis Signal reconstruction Entropy Splines (mathematics) Speech processing Software development management

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An Optimized Edge Platform for 3D Object Detection in Autonomous Driving

An Optimized Edge Platform for 3D Object Detection in Autono...

引用

International conference on Electronics, information and Communications (ICEIC)

作者： Kwonseung Bok Yumi Kim Aeri Kim Sang-Seol Lee Kyungho Kim Intelligent Image Processing Research Center Korea Electronics Technology Institute Gyeonggi-do South Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Autonomous driving technology has been developed to enhance the safety of vehicles and drivers by recognizing surrounding objects and making appropriate situational judgments using sophisticated sensors. In particular, 3D object detection networks utilizing LiDAR within deep learning models have advanced to achieve accurate object detection. However, performing 3D object detection using LiDAR and advanced sensors requires processing a massive amount of data, making it challenging to handle directly within the vehicle. While external computational resources such as cloud servers can be employed, this approach introduces difficulties in guaranteeing low data exchange latency and ensuring data stability and security. In this paper, we propose a method to process 3D object detection on an edge platform, enabling it to be handled locally for autonomous driving applications. The proposed edge platform consists of a hardware architecture optimized for processing large volumes of point cloud data and a quantized, lightweight network. This enables the edge platform to handle the entire process of acquiring point cloud data via LiDAR, performing 3D object detection, and executing image post-processing tasks directly on the device.

关键词： Point cloud compression Solid modeling Three-dimensional displays Laser radar Image edge detection Computational modeling Object detection sensors Object recognition Autonomous vehicles

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Optimized Hybrid Deep Learning Approach for Detecting DoS Attacks Using Feature Selection in Wireless sensor networks

Optimized Hybrid Deep Learning Approach for Detecting DoS At...

引用

Computational, Communication and information Technology (ICCCIT), International conference on

作者： S Sasson Taffwin Moses L Abraham David G S R Emil Selvan M P Ramkumar Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai Tamil Nadu India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

The rapid growth of smart cities, healthcare monitoring, and environmental sensing relies heavily on the real-time data processing capabilities of Wireless sensor networks (WSNs). However, these networks face significant security challenges, particularly from malicious activities like Denial of Service(DoS) attacks, which can disrupt critical operations. A hybrid intrusion detection model that combines Convolutional Neural networks (CNN) and Deep Neural networks (DNN) to improve the detection of such threats has been proposed. The preprocessing phase includes the Synthetic Minority Over-sampling Technique (SMOTE) which mitigates class imbalances ensuring robust performance across diverse attack scenarios. The model incorporates advanced feature selection techniques including SelectKBest and Grey Wolf Optimization (GWO) to identify the most critical features for enhanced detection accuracy. The CNN component effectively captures local traffic patterns, which are then refined by the DNN for accurate classification. This hybrid model's effectiveness in detecting diverse DoS attack scenarios is evaluated using WSN-DS dataset, ensuring robust security for WSNs. By achieving an impressive 98% detection accuracy, this hybrid model proves to be effective in intrusion detection. Its applications can significantly strengthen the security and reliability of WSNs, ensuring uninterrupted operations in key sectors such as smart cities, healthcare, and environmental monitoring.

关键词： Wireless sensor networks Accuracy Smart cities Intrusion detection Medical services Traffic control Feature extraction sensors Convolutional neural networks Reliability

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Spectrum Sensing and Management in Cognitive Radio networks using Genetic Algorithm based Soft Decision Fusion

Spectrum Sensing and Management in Cognitive Radio Networks ...

引用

intelligent Systems and Computational networks (ICISCN), International conference on

作者： Rana Veer Samara Sihman Brahmatej Rupavath Suganya. E Saef Obidhusin Payal Soni Gupta R. Anitha Indiana Wesleyan University Marion USA Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College of Engineering and Technology India Department of Information Technology New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent years, the advancements of wireless technologies have led to a significant demand in radio frequency spectrum. Cognitive Radio networks (CRN) are used among the intelligent wireless communication systems that enhanced spectrum efficiency. However, CRN with their help in enhancing the spectrum but possesses various challenges such as noise, dynamic environments that complicate the process of sensing. Therefore, in this research Genetic Algorithm based Soft Decision Fusion (GASDF) model is proposed for an effective Spectrum Sensing and Management in CRN. Primarily, the input data is collected through radio network signals, then the signals are preprocessed using the Z-score normalization to reduce the noise from the signals. Further, the relevant features are extracted using Continuous Wavelet Transform (CWT). Then, spectrum allocation is done using Differential Evolution (DE) and the spectrum sensing is done through GASDF. From the results, proposed model attained better outcomes compared to existing Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) with 2.45% Probability of false alarm (Pf) rate.

关键词： Wireless sensor networks Time-frequency analysis Continuous wavelet transforms Noise Feature extraction sensors Cognitive radio Resource management Convolutional neural networks Genetic algorithms

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Enhancing Performances of Deep Neural networks with Ensemble Learning Methods for Complex Human Activity Recognition Using Wearable sensors

Enhancing Performances of Deep Neural Networks with Ensemble...

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International ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

sensor-based human activity recognition (HAR) is a widely explored research area focused on identifying human behaviors by analyzing sensor-generated data, particularly one-dimensional time series signals. Such HAR systems have many real-world applications, including elderly health monitoring and athlete performance evaluation. This paper presents Ens-DeepNet, a novel ensemble learning approach designed for recognizing complex human activities using data from wearable sensors. While deep neural networks have proven effective for HAR tasks, their performance can be further improved through ensemble strategies. To this end, we propose an ensemble architecture that combines five distinct deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). These models are integrated using a majority voting scheme to boost classification accuracy. The proposed method is evaluated using the UTwente dataset, which includes sensor data collected from ten individuals via tri-axial accelerometers and gyroscopes. Experimental findings demonstrate that the ensemble model achieves an impressive accuracy of 99.28%, surpassing the performance of each standalone model, whose accuracies range from 92.77%to 97.34%. This performance gain is particularly notable in recognizing complex activities such as eating, drinking coffee, smoking, and conversing.

关键词： Deep learning Performance evaluation Accuracy Time series analysis Bidirectional long short term memory Performance gain Human activity recognition Ensemble learning Older adults Wearable sensors

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Systematic Study on Improving the All-Weather Monitoring Accuracy of Horse Walking Rooms Using Signal processing Technology

Systematic Study on Improving the All-Weather Monitoring Acc...

引用

Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe conference on

作者： Zhuo Sun Xin Yan Wuhan Business University Wuhan China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

This study proposes an all-weather monitoring system for horse walking rooms based on signal processing technology. The fast Fourier transform (FFT) algorithm is used to optimize the monitoring accuracy and achieve efficient and accurate real-time monitoring. The system consists of a sensor array, a signal processing module, and a data analysis module, which can monitor key parameters such as temperature, humidity, and motion status. First, the system collects the original signals of multiple sensors, suppresses noise through a filtering algorithm, and extracts effective information; then, the FFT algorithm is used to perform frequency domain analysis on the signal, which improves the efficiency and accuracy of extracting key signal features. The simulation test results show that the monitoring error of the optimized system is reduced to 0.85% under typical conditions, which is significantly better than the 1.8% of the traditional method. In high-noise and complex environments, the system signal interference reduction rate is 45%, and the robustness is improved by about 30%. The experiment verifies the monitoring accuracy and stability of the system under all-weather conditions, providing strong support for the intelligent management of horse walking rooms.

关键词： Legged locomotion Temperature sensors Accuracy Fast Fourier transforms Signal processing algorithms Signal processing Feature extraction Real-time systems Robustness Monitoring

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