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检索条件"任意字段=Conference on Radar Sensor Technology and Data Visualization"

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Design and Development of IOT Based Smart Sericulture Plant

Design and Development of IOT Based Smart Sericulture Plant

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International conference on Computation System and Information technology for Sustainable Solutions (CSITSS)

作者： Ashwini P Adarsh B U Hima N C Likitha J Shristi B Padashetty Srinikethan Deshpande School of ECE REVA University Bengaluru India Happiest Minds Technologies Bengaluru India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Sericulture refers to the practice of raising silkworms and extracting silk from them. India stands as the world's second-largest producer of raw silk, with approximately 9.76 million individuals engaged in employment within the Indian silk industry. The sector does, however, confront numerous difficulties, such as the volatility of raw silk prices and competition from synthetic fibers. Silkworms usually employed in sericulture are the caterpillars of the domestic silk moth, also known as ‘Bombyx Mori’. One of the finest methods to make a solid living is through sericulture, which offers superior returns on investment as well as self employment opportunities. In this procedure, silkworm larvae are provided with mulberry leaves for sustenance. Upon reaching their fourth moult, they ascend a nearby twig to commence the spinning of their silk cocoons. There are numerous ways to improve the silkworm rearing methods now in use. The silk worm, however, must go through numerous stages in order to change from a larva to a silk worm. The farmer's biggest task during each stage is monitoring the silkworms. This paper includes design and development of an automated sericulture system approach based on IoT utilizing Raspberry Pi in this study. It deals with controlling weather factors like humidity and temperature on farms. This method uses automated actuators and sensors to regulate the environment in the sericulture using heating and cooling system. It also includes the vibration sensor, which identifies and reacts quickly to any disruptions in the system and is indicated through automated alert system i.e. Buzzer. The incorporation of a serial camera with the Raspberry Pi, which undergoes image processing to offer visual status updates through texture analysis and sprinkler which acts as feeder system provides adequate nutrition to the silkworms for the optimal and healthy growth. Through Blynk, the end user will receive the data wirelessly from these sensors. Each of these

关键词： Temperature sensors Vibrations Cloud computing visualization Image processing Employment sensor systems Grippers Monitoring Testing

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The wear recognition on guide surface based on the feature of radar graph

The wear recognition on guide surface based on the feature o...

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2015 International conference on Testing and Measurement: Techniques and Applications(TMTA 2015)

作者： Y.H.Zhou W.M.Zeng Q.Xie School of Mechanical Engineering Xiangtan University

ISBN: (纸本)9781138028128

In order to solve the wear recognition problem of the machine tool guide surface, a new machine tool guide surface recognition method based on the radar-graph barycenter feature is presented in this paper. Firstly, the gray mean value, skewness, projection variance, flat degrees and kurtosis features of the guide surface image data are defined as primary characteristics. Secondly, data visualization technology based on radar graph is used. The visual barycenter graphical feature is demonstrated based on the radar plot of multi-dimensional data. Thirdly, a classifier based on the support vector machine technology is used, the radar-graph barycenter feature and wear original feature are put into the classifier separately for classification and comparative analysis of classification and experiment results. The calculation and experimental results show that the method based on the radar-graph barycenter feature can detect the guide surface effectively.

关键词： image The wear recognition on guide surface based on the feature of radar graph

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Fast Object Classification for Autonomous Driving Using Shape and Motion Information Applying the Dempster-Shafer Theory

Fast Object Classification for Autonomous Driving Using Shap...

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International conference on Intelligent Transportation

作者： Michael Kusenbach Thorsten Luettel Hans-Joachim Wuensche Institute for Autonomous Systems Technology University of the Bundeswehr Munich Germany

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

The classification of moving objects is one of the crucial tasks to enable autonomous driving in urban environments. Even in simple traffic scenarios we already observe a multitude of dynamic objects such as pedestrians, bicyclists or cars. The challenge is to accurately determine the correct class of each object under all circumstances. This problem gets even harder if an object transitions from one class to another, e. g., a person that first pushes a bicycle and then starts to ride it. The question we are facing here is: How can we represent this situation in terms of classification? We propose a universal way of describing the characteristics of the classes by designing specialized belief functions to model object features. Given the belief functions, so-called “mass values” can be derived for each class. Finally, using the Dempster-Shafer theory, a “belief” is assigned to the power set of the classes. The approach provides a direct correlation to the features and is, therefore, able to provide a comprehensible explanation for the result. We evaluate the proposed approach given data gathered with a LiDAR sensor. We present its classification performance, show its effectiveness and discuss advantages and disadvantages.

关键词： Automobiles Feature extraction Laser radar Three-dimensional displays Autonomous vehicles Task analysis Two dimensional displays

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Real-Time Monitoring visualization Through On-Line Simulation

Real-Time Monitoring Visualization Through On-Line Simulatio...

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2008 IEEE Instrumentation and Measurement technology conference (I2MTC 2008), vol.3

作者： Azzedine Boukerche Regina B. Araujo Fabio M. Iwasaki Ednaldo Pizzolato SITE University of Ottawa Ottawa Canada CS Federal University of Sao Carlos Brazil

On-line simulation is being used to "augment", i.e., improve the management and control quality of operational systems. Real-time monitoring of physical environments that are subject to emergency situations can greatly benefit from on-line simulations. These can be fed with events gathered and interpreted from sensor networks what can lead to faster incidents and accidents prediction. This paper deals with the visualization of on-line simulations. A web-based framework is described, which generates HLA compliant on-line simulation visualization interfaces. These interfaces can be deployed on different devices, from cellular phones to digital caves.

关键词： Monitoring Web services Disaster management data visualization Java Accidents Real time systems sensor systems Runtime XML

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New methods and tools for the development and verification of safety functions during development of pedestrian detection systems

New methods and tools for the development and verification o...

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International Joint conference on INC, IMS and IDC, NCM

作者： Hadj Hamma Tadjine Rene Roellig Karsten Schulze Herold Daniel Active safety & Driver Assistance IAV GmbH Berlin Germany

The protection of pedestrians from injuries by accidental collision is a primary focus of the automotive industry and of government legislation [1]. In this area, scientists and developers are faced with a multitude of requirements. Complex scenes are to be analyzed. The wide spectrum of where pedestrians and cyclists appear on the road, weather, and light conditions are just examples. data fusion of raw or preprocessed signals for several sensors (cameras, radar, lidar, ultrasonic) need to be considered as well. Accordingly, algorithms are very complex. When moving from prototypic environments to embedded systems, additional constraints must be considered. Limited system resources drive the need to simplify and optimize for technical and economic reasons. With all these constraints, how can the safety functions be safe-guarded? This submission considers scene-based methods for the development of vehicle functions from prototype to series production focusing on functional safety. Starting points are modeled use cases which can be evaluated by simulation and variation of internal parameters as well as external influences, like velocity, course, likelihoods, measurement errors and latencies. Based on these methods, the approach of scene based specifications is discussed. Scene based test catalogs are also considered. These test catalogs can consist of synthetic (simulated) test cases and recorded sequences. The effect of changes during a development project can be more reliably evaluated with stable and reproducible test cases. The article presents typical sample usage scenarios and discusses the results achieved so far.

关键词： Vehicles Safety Catalogs sensor systems Cameras Hardware

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Health Monitoring System Using Internet of Things (IoT) Sensing for Elderly People

Health Monitoring System Using Internet of Things (IoT) Sens...

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International conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Ibrar Hussain Sidra Tahir Mamoona Humayun Maram Fahhad Almufareh N. Z. Jhanjhi Farkhanda Qamar University Institute of Information Technology University of Arid Agriculture Rawalpindi Pakistan Department of Information Systems College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia School of Computer Science and Engineering (SCE) Taylor's University Subang Jaya Malaysia

ISBN: (纸本)9781665460729

Smart and linked health care is one of the many uses that the Internet of Things (IoT) has made possible. Getting everything in a hurry is one of the biggest demands of the contemporary world. People nowadays want to understand their current health state and demand prompt access to appropriate treatment. Even they have a great deal of challenges while trying to let someone know about their ailment. Networked sensors enable the collection of detailed data pertaining to our physical and mental health, whether they are worn on the body or integrated into our living spaces. Such data may result in a beneficial transformational shift in the health care environment if continuously collected, pooled, and skillfully mined. Specifically, the accessibility of information at already unheard of scales and worldly longitudes joined with another age of savvy handling calculations can: (a) work with a development in clinical practice from the momentum post facto analyze and-treat responsive worldview to a proactive structure for visualization of sicknesses at an early stage, combined with counteraction, fix, and generally speaking administration of wellbeing rather than illness; (b) empower personalization of medical services; and (c) work on the personal satisfaction for individuals by decreasing. In this study, we address the advantages and disadvantages of IoT in achieving this future health care goal.

关键词： Temperature sensors Temperature measurement sensor systems Internet of Things Servers Older adults Monitoring

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Simultaneous Localization and Mapping [SLAM] of Robotic Operating System for Mobile Robots

Simultaneous Localization and Mapping [SLAM] of Robotic Oper...

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International conference on Advanced Computing and Communication Systems (ICACCS)

作者： S. Gobhinath K. Anandapoorani K. Anitha D. Dhivya Sri R. DivyaDharshini Sri Krishna College of Engineering and Technology Coimbatore

These days' mobile robots have seen a great proliferation in real world scenarios they impact lives as they tend to interfere in environments that we live. Mobile can become really handy in situations where there is need for rapid movements. The areas are open new avenues to varied business fortunes the mobile robot market is research in these areas are vital to capture a big market in the world, robot's intelligence is vital in movements of the robot such as motion and navigation, a robot set to gain knowledge about its environment by means of sophisticated technologies like Simultaneous Localization and Mapping [1]. The robot tends to obtain knowledge upon the system by various sensor data and it has various algorithms like SLAM this helps to map the surrounding and it has overview about the indoor environment the input to the SLAM is a laser scan data that is obtained from LIDAR the existing system (TURTLEBOT) tends to perform similar objective but the computation system that is the laptop is on the mobile robot which makes it difficult to operate hence it has operational cost and operational difficulties the proposed model tends to work on a considerably low cost and high interoperability as the system uses ROS based model to get and transfer data. SLAM is better solution in computational world as it helps robots achieve mapping and localizing at the same time this helps better navigation of system [2]. This feature of SLAM helps robots to navigate effectively this data can be utilized to draft out decisions which helps autonomous manoeuvre of domestic robots. This helps to achieve better path planning and navigation & mapping, We are now entering a new era of robust vision, in which working with a higher accepting computer process according to hearing aids and resources, and work-driven understanding is needed. This is available with a Probabilistic road map with this algorithm with the help of pose graph optimization and a loop closure of the help mode. Use th

关键词： Simultaneous localization and mapping Laser radar Portable computers Navigation Robot kinematics Computational modeling Roads

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SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation

SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range H...

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IEEE International conference on Robotics and Automation (ICRA)

作者： Hao Dong Weihao Gu Xianjing Zhang Jintao Xu Rui Ai Huimin Lu Juho Kannala Xieyuanli Chen ETH Zürich HAOMO.AI National University of Defense Technology Aalto University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

High-definition (HD) semantic map generation of the environment is an essential component of autonomous driving. Existing methods have achieved good performance in this task by fusing different sensor modalities, such as LiDAR and camera. However, current works are based on raw data or network feature-level fusion and only consider short-range HD map generation, limiting their deployment to realistic autonomous driving applications. In this paper, we focus on the task of building the HD maps in both short ranges, i.e., within 30m, and also predicting long-range HD maps up to 90m, which is required by downstream path planning and control tasks to improve the smoothness and safety of autonomous driving. To this end, we propose a novel network named SuperFusion, exploiting the fusion of LiDAR and camera data at multiple levels. We use LiDAR depth to improve image depth estimation and use image features to guide long-range LiDAR feature prediction. We benchmark our SuperFusion on the nuScenes dataset and a self-recorded dataset and show that it outperforms the state-of-the-art baseline methods with large margins on all intervals. Additionally, we apply the generated HD map to a downstream path planning task, demonstrating that the long-range HD maps predicted by our method can lead to better path planning for autonomous vehicles. Our code and self-recorded dataset have been released at https://***/haomo-ai/SuperFusion.

关键词： Laser radar Limiting Semantics Estimation Cameras Path planning Safety

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Cooperative Trajectory Prediction Using IVC and Accuracy-Aware Attention with Inaccurate GPS data

Cooperative Trajectory Prediction Using IVC and Accuracy-Awa...

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IEEE Internatonal conference on Mobile Adhoc and sensor Systems (MASS)

作者： Jae-Han Lim Katsuhiro Naito Dong-Hyuk Tak Yeon-Sup Lim Kwangwoon Univ. Aichi Institute of Technology Sungshin Women's Univ.

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

As autonomous driving vehicles have emerged, Under-Resourced Automated Vehicle (URAV) and Connected Automated Vehicle (CAV) could coexist in similar regions. For safe driving in the coexistence scenario, URAV and CAV should accurately predict future trajectories of surrounding vehicles (i.e., neighbors). To predict the trajectories by URAV, two primitives are required: 1) positioning via low-price devices (e.g., GPS) and 2) Inter-Vehicle Communication (IVC). However, using only these primitives could induce inaccurate trajectory prediction because of inaccurate GPS data and failure in packet transmission via IVC. To address the limitations of URAV, in this paper, we propose a novel scheme for trajectory prediction whereby URAV cooperates with CAV by exploiting the powerful resources of CAV via IVC. For improving the accuracy of cooperative trajectory prediction, our scheme employs three modules: 1) Cooperative Position Share and Compensation (CPSC) to mitigate positioning error of GPS data, 2) Accuracy-aware Social Predictor (ASP) to differentiate the influence of neighbors on prediction based on their position accuracies, and 3) Worst Error Mitigation (WEM) to reduce maximum prediction error. To our knowledge, our scheme is the first to enable accurate trajectory prediction of neighbors with limited resources via cooperation with CAV. Intensive evaluation using QUALNET and Pytorch demonstrates that our scheme significantly outperforms previous approaches in terms of average and maximum prediction errors.

关键词： Accuracy Laser radar Prevention and mitigation Smart systems Trajectory Autonomous vehicles Global Positioning System

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Learning Human Navigation Behavior Using Measured Human Trajectories in Crowded Spaces

Learning Human Navigation Behavior Using Measured Human Traj...

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2020 IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Muhammad Fahad Guang Yang Yi Guo National Oilwell Varco Houston TX USA Stevens Institute of Technology Hoboken NJ USA

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

As humans and mobile robots increasingly coexist in public spaces, their close proximity demands that robots navigate following navigation strategies similar to those exhibited by humans. This could be achieved by learning directly from human demonstration trajectories in a machine learning framework. In this paper, we present a method to learn human navigation behaviors using an imitation learning approach based on generative adversarial imitation learning (GAIL), which has the ability of directly extracting navigation policy. Specifically, we use a large open human trajectory dataset that was experimentally collected in a crowded public space. We then recreate these human trajectories in a 3D robotic simulator, and generate demonstration data using a LIDAR sensor onboard a robot with the robot following the measured human trajectories. We then propose a GAIL based algorithm, which uses occupancy maps generated using LIDAR data as the input, and outputs the navigation policy for robot navigation. Simulation experiments are conducted, and performance evaluation shows that the learned navigation policy generates trajectories qualitatively and quantitatively similar to human trajectories. Compared with existing works using analytical models (such as social force model) to generate human demonstration trajectories, our method learns directly from intrinsic human trajectories, thus exhibits more human-like navigation behaviors.

关键词： Performance evaluation Analytical models Laser radar Three-dimensional displays Navigation Robot sensing systems Trajectory

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