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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications"

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Intelligent Fruit Quality Monitoring System using sensor Networks and Deep Learning

Intelligent Fruit Quality Monitoring System using Sensor Net...

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2024 International conference on Smart Electronics and Communication systems, ISENSE 2024

作者： Kumar, Arvind Jaya Krishna, N. Chandra, Mahesh Kumar, Rampravesh Sahoo, Jaya Prakash Poojitha, N. Karan, Biswajit Kishore, Kamlesh Gitam University Department of EECE Bengaluru India School of ECE Reva University Bengaluru India Sandip University Department of EEE Nashik India University of Stellenbosch Department of EEE Stellenbosch South Africa RTC Institute of Technology Department of ECE Bengaluru India

ISBN: (纸本)9798331528126

This paper presents a novel approach for fruit quality detection using a multi-modal system that combines sensor data and image processing techniques with deep learning. In the first stage of our experiment, we employed various sensors to measure temperature, gas levels, and moisture content using DHT 11 and MQ-135 sensors. These sensors provide valuable insights into the environmental conditions surrounding the fruits, which are crucial indicators of their freshness. In the second stage, we utilized image processing algorithms to pre-process fruit images and train a SqueezeNet convolutional neural network (CNN) to classify fruits into two classes: fresh and rotten. By integrating sensor data with image-based classification, our proposed method offers a comprehensive solution for real-time fruit quality assessment. Experimental results demonstrate the effectiveness of our approach in accurately detecting fruit quality, paving the way for advanced applications in food safety and quality control. © 2024 IEEE.

关键词： Convolutional neural networks

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Characterising an event-based detector for applications to wavefront sensing 9

Characterising an event-based detector for applications to w...

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conference on Adaptive Optics systems IX

作者： Cockram, Monique Rey, Noelia Martinez Australian Natl Univ Adv Instrumentat & Technol Ctr Res Sch Astron & Astrophys Canberra ACT Australia

ISBN: (纸本)9781510675186;9781510675179

Event-based sensors detect only changes in the brightness across a field of view, with each pixel producing an asynchronous stream of spatial-temporal events data, rather than recording frames of overall illumination of a scene like a traditional frame-based sensor. The location, timestamp, and polarity (positive or negative change) of the recorded events provides low bandwidth data that is useful in real-time sensing of changes in the field of view. These features result in advantages such as high temporal resolution, low latency, high dynamic range, and low power consumption. The use of these sensors has been explored in areas such as object tracking, robotics, and space situational awareness. We are proposing a new application of the event-based sensor in wavefront sensing for adaptive optics. At the advanced Instrumentation and Technology Centre (AITC) in the Australian National University (ANU), a test bench has been built to test this type of sensor. This study will present the results of this laboratory testing to characterise an event-based sensor's capabilities to detect tip-tilt aberrations in different conditions, such as background illumination levels. In particular, the event-based sensor's immunity to a constant background illumination was investigated by introducing an external light source into the scene and recording its performance for different signal strengths. We will present the results of the bench testing, the error budget for tip-tilt sensing using these event-based detectors, and a characterisation of the response of event-based sensors to different illumination conditions.

关键词： event-based detectors tip-tilt adaptive optics

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advanced Camouflage Robot for Military Spying applications using sensor Fusion and Machine Learning 3

Advanced Camouflage Robot for Military Spying Applications u...

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3rd IEEE International conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Kulkarni, Neeraj P Khan, Subim N. Rathod, Rahul Ramkishan Choure, Ajay Raosaheb Chinchole, Mahesh Gokul Jspm Rajarshi Shahu College of Engineering Tathawade Pune India Soet D Y Patil University Ambi Pune India Bhivarabai Sawant Institute Of Technology And Research E&tc Department Wagholi Pune India Nutan Maharashtra Institute of Engineering and Technology E&tc Department Pune India

ISBN: (纸本)9798350354348

The current defense application scenario. the ability to blend into the environment is crucial for reducing detection and enhancing operational efficiency. This paper presents the development of an advanced camouflage robot to dynamically adapt its color to match its surroundings. The proposed system utilizes a combination of machine learning, computer vision, and sensor fusion to achieve camouflaging using the RGB LED panels mounted on the robot to adapt to the color of the surroundings. The robot is equipped with a video camera system that captures real-time video feed of the environment. The captured video is then processed by a trained machine learning model, which analyzes the color distribution in the surroundings. Simultaneously, the robot uses RGB color sensors to accurately detect the colors present in its immediate vicinity. The data from both the camera and the sensors are integrated to determine the predominant color around the robot. The hybrid camouflaging approach involves feeding the combined data into the machine learning model, which then calculates the color of maximum distribution. The robot then activates its RGB panels, adjusting their color to blend with the identified predominant color in the environment thereby making it difficult to locate. This process allows the robot to effectively camouflage itself, minimizing its visual footprint. KNN and sensor fusion techniques are used to achieve this task. © 2024 IEEE.

关键词： Nearest neighbor search

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sensor Automation for Industrial applications and Prediction of Energy Level for Home Appliances Using Machine Learning Algorithm 9

Sensor Automation for Industrial Applications and Prediction...

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9th International conference on advanced Computing and Communication systems, ICACCS 2023

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Selvakumari Jeya, I Jasmine Niranjani, V. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

In recent years wireless sensor networks have helped with automation in many industry domains. Wireless technology cuts cable costs, deployment time and information to transfer and process from source to destination. To make good intelligent and highly automated systems, sensors are crucial. They make it possible to detect, analyze, measure, and process a variety of alterations that occur in industrial equipment's location, length, height, appearance, displacement, etc. This paper described the industrial standardization of different WSN protocols like Zig-Bee, Wireless HART, UWB, IETF 6LowPAN, ISA100, Bluetooth and Wi-Fi. The work represented a few applications of WSNs in industry and its implementation challenges like safety, security, and availability. The work collected the energy consumption data of different home appliances. We applied the LightGBM machine learning algorithm to find a better energy consumption and prediction result. © 2023 IEEE.

关键词： Energy utilization

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Human Activity Recognition Using sensor Fusion and Deep Learning for Ergonomics in Logistics applications

Human Activity Recognition Using Sensor Fusion and Deep Lear...

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2023 IEEE International conference on Internet of Things and Intelligence systems, IoTaIS 2023

作者： Arab, Ahmad Schmidt, Antonius Aufderheide, Dominik South Westphalia University of Applied Sciences Faculty of Electrical Engineering Soest Germany

ISBN: (纸本)9798350319040

The rapid growth within the e-commerce industry and the necessity of hiring more inexperienced labor in logistics made the warehouse safety more critical than ever. Monitoring the workers' activity during warehouse operations is a fundamental task to create an ergonomic workplace and to reduce operational costs. Therefore, an advanced Human Activity Recognition (HAR) solution is needed to monitor such industrial environments. In this paper, the development of an accurate and reliable HAR classifier using sensor fusion methods is investigated. Three Inertial Measurement Units (IMUs) are used to capture body movement while performing typical warehouse tasks. Deep Learning (DL) algorithms are employed to model the raw time series data and two sensor fusion techniques are applied to combine the data from the different sensors. Overall, the optimal HAR classifier was found to be a stacked model, being more practical and less computationally expensive for industrial applications. © 2023 IEEE.

关键词： Ergonomics

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Lightweight Thermal Super-Resolution and Object Detection for Robust Perception in Adverse Weather Conditions

Lightweight Thermal Super-Resolution and Object Detection fo...

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IEEE/CVF Winter conference on applications of Computer Vision (WACV)

作者： Shyam, Pranjay Yoo, HyunJin Faurecia IRYStec Inc Montreal PQ Canada

ISBN: (纸本)9798350318920;9798350318937

In this work, we examine the potential application of thermal cameras in improving perception capabilities in adverse weather conditions like snow, night-time driving, and haze, focusing on retaining the performance of advanced Driver Assistance systems (ADAS), thus enhancing its functionality and safety characteristics. While thermal sensors offer the advantage of robust information capture in adverse weather conditions, their integration is plagued with issues surrounding poor feature capture in normal conditions, low imaging resolution, and high sensor costs. We address the former by formulating the problem definition as information switching wherein thermal images are selected when visible images are degraded. Furthermore, we consider a single object detector for RGB and thermal images to ensure low latency. We propose utilizing a learnable projection function that translates the thermal image into RGB color space, thus providing minimal modifications to the underlying object detector. We address the issues of low imaging resolution and cost by proposing a novel procedure that combines super-resolution and object detection, enabling the utilization of low-resolution and low-cost uncooled thermal imaging sensors. To ensure the complete pipeline meets the actual deployment requirements of real-time inference on resource-constrained devices, we introduce a lightweight super-resolution algorithm, implementing optimizations within the network structure followed by global pruning. In addition, to improve the feature representations extracted by lightweight encoders, we propose a bidirectional feature pyramid network to enhance the feature representation. We demonstrate the efficacy of the proposed mechanism through extensive simulated evaluations on automotive datasets such as FLIR, KAIST, DENSE, and Freiburg Thermal.

关键词： applications Autonomous Driving

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Vehicle Dynamical sensor Monitoring Toward Forest Soil Moisture Measuring System 12

Vehicle Dynamical Sensor Monitoring Toward Forest Soil Moist...

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12th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems: Technology and applications, IDAACS 2023

作者： Kiga, Yoshinori Tazawa, Takumi Iwase, Masami School of Science and Technology for Future Life Tokyo Denki University Department of Robot Mechatronics Tokyo120-8551 Japan

ISBN: (纸本)9798350358056

In this research, we develop a sensor monitoring system for vehicle dynamics aimed at developing a forest soil moisture measurement system for landslide disaster prevention. The identification of landslide hazard warning areas is necessary to prevent landslide disasters. Soil moisture is one of the factors. Common methods of surveying this soil moisture content include borings and satellites. However, borehole investigations may omit areas that truly need to be investigated without a priori knowledge of the locations to be measured. In addition, it is difficult to specify the timing of measurements in satellite surveys. Therefore, this research aims to establish a system that is safe, wide-ranging, and capable of setting the timing and range of measurement as needed. One approach to meet these requirements is to mount sensors on remotely operated vehicles to measure soil moisture content. In general, vehicles for such purposes need to travel in rough environments, and in order to travel well in rough environments, it is necessary to detect abnormal driving conditions such as slipping or skidding. For this purpose, we develop a sensor monitoring for the vehicle traveling in rough terrain and through forest. © 2023 IEEE.

关键词： filtering IMU Vehicle dynamics monitoring

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Review on Communication Technologies used in Electric Vehicles 8

Review on Communication Technologies used in Electric Vehicl...

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8th IEEE International conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Prerana, S. Reddy, Niranjan N. Varghese, Susan G. Sabhahit, Jayalakshmi N. Manipal Institute of Technology MAHE Department of Electrical and Electronics Engineering Manipal576104 India

ISBN: (纸本)9798350350593

The automotive industry relies on wide range of networking protocols to facilitate effective communication and data exchange within vehicles. Local Interconnect Network (LIN) is a cost-effective protocol commonly utilized for non- critical tasks such as controlling interior lighting and transmitting basic sensor data. Similarly, Control Area Network (CAN) is a widely accepted protocol that enables high-speed real-time networking between electronic control units (ECUs), sensors, and actuators. CANopen is a protocol built after CAN, offers standardized communication profiles and advanced features to seamlessly integrate devices in embedded systems across various industries, including automotive. Time- Triggered CAN (TTCAN) enhances traditional CAN by incorporating time-triggered communication capabilities, ensuring deterministic behavior and predictable response times crucial for safety-critical applications. SAE J1939, which is based on CAN is commonly used in heavy-duty vehicles for standardized communication and diagnostics among different vehicle components. FlexRay, known for its high-speed and deterministic characteristics, is used in safety-critical automotive systems like active suspension and steer-by-wire applications, providing fault tolerance and advanced control functionalities. A comprehensive understanding of these automotive networking protocols is vital for establishing reliable and efficient communication networks that support a wide array of functionalities and safety features in modern vehicles. © 2024 IEEE.

关键词： Active suspension systems

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Unlocking Immersive Driving Experiences with Automotive sensor-Powered Extended Reality (XR) 26

Unlocking Immersive Driving Experiences with Automotive Sens...

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26th Irish Machine Vision and Image Processing conference, IMVIP 2024

作者： Manila, Anushalakshmi Negi, Sonam Nowakowski, Christopher Reilhac, Patrice Valeo North America Inc. United States

ISBN: (纸本)9781837242672

While industries like healthcare and manufacturing are harnessing Extended Reality (XR) for novel applications, automotive XR opportunities remain largely untapped. XR holds immense potential beyond the current industry focus on marketing campaigns and virtual showrooms. Modern vehicles are increasingly equipped with multiple sensors for advanced driver assistance and autonomous driving. In this paper, we demonstrate how these automotive sensors and algorithms can be used to create Augmented Reality (AR) effects such as virtual object placement and occlusion, and we describe Valeo Racer, a vehicle-based XR gaming concept built using these methods https://***/jFQ8wxCANic [Valeo, 2024]. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： advanced driver assistance systems

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Approach to Acoustic Drone Detection System Development 12

Approach to Acoustic Drone Detection System Development

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12th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems: Technology and applications, IDAACS 2023

作者： Kochan, Roman Hots, Nataliya Fabirovskyy, Sergiy Storozh, Volodymyr Kosterov, Alexander Ozirkovskyy, Leonid Helesh, Anna Arkadii, Shulhai Kochan, Volodymyr Strykhalyuk, Bohdan Mykyichuk, Mykola Demydov, Ivan University of Bielsko-Biala Bielsko-Biala42-300 Poland Lviv Polytechnic National University Lviv79013 Ukraine State Scientific Research Institute for Metrology of Measuring and Control Systems Sistema Lviv79000 Ukraine I. Horbachevsky Ternopil State Medical University Ternopil46001 Ukraine West Ukrainian National University Ternopil46003 Ukraine

ISBN: (纸本)9798350358056

An analysis of drone detection methods was carried out in this paper. There is a reasoned choice of the optimal method for implementation based on the analysis of acoustic signals generated by drones. The set of methods of acoustic signal analysis is presented. These methods provide the measurement of drone coordinates and complement each other depending on the flight conditions. The structure of a distributed sensor network oriented on the online detection of drones and their tracking has been synthesized. The developed system provides the appropriate complexity as well as large flexibility and scalability. © 2023 IEEE.

关键词： Acoustic signal Distributive sensor network Drone detection

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