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Research on sensor Data Spatiotemporal Synchronization Technology for Onboard Visual Navigation systems

Research on Sensor Data Spatiotemporal Synchronization Techn...

引用

Power Electronics, Computer Applications (ICPECA), ieee international conference on

作者： Yanna Yuan Huimin Liu Chenqiang Zhao Aviation Industry Computing Technology Research Institute Xi'an China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Against the backdrop of the rapid development of unmanned aerial vehicle (UAV) technology, onboard visual navigation systems have become the core support technology for achieving precise positioning and efficient map construction of UAVs. this paper deeply explores the key technologies of sensor data spatiotemporal synchronization in onboard visual navigation systems, aiming to solve the synchronization issues in multi-sensor data fusion in variable environments, and enhance the navigation accuracy and reliability of UAVs. the research covers both spatial and temporal synchronization dimensions, with a focus on the technical implementation of Visual-Inertial Odometry (VIO). For spatial synchronization, a target-based method is proposed to construct an error equation for solving the extrinsic parameters between the camera and the Inertial Measurement Unit (IMU), achieving precise alignment of sensor coordinate systems through the identification and matching of visual target feature points. In terms of temporal synchronization, considering the data frequency differences between the camera and IMU, this paper designs a time alignment strategy that realizes efficient temporal alignment of data through data buffering and timestamp matching. Additionally, the initialization and local sliding window optimization process of the Visual-Inertial Odometry are detailed, achieving high-precision fusion of visual and inertial data through feature extraction, optical flow tracking, and motion structure recovery. Experimental results show that the methods proposed in this paper can technically provide strong support for the autonomous flight of UAVs, effectively improving the navigation performance of UAVs in complex environments, and enhancing the robustness of the system.

关键词： Visualization Accuracy Navigation Cameras Feature extraction Solids Robustness Spatiotemporal phenomena Synchronization Odometry

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Mimicking Human Skin Touch using Microfluidic Single Microchannel sensor

Mimicking Human Skin Touch using Microfluidic Single Microch...

引用

India Council international Subsections conference (INDISCON), ieee

作者： Deepika Gupta Sachin Anuj Nandanwar Deepak Sachan Jagadeesh Kadiyam G Shrikanth Reddy Narendra Kumar Dhar Indian Institute of Technology Mandi

ISBN: (数字)9798350376753

ISBN: (纸本)9798350376760

this work focuses on using a liquid metal-based microfluidic single-channel sensor encapsulated in the elastomer material for sensing distinct motions of the hand. To explore its efficiency, the sensor is placed at different positions (on the palm or at the back of the hand, etc.). A mathematical model is derived to understand the electro-mechanical characteristics of the sensor. the model depends on the deformation of the elastic microchannel because strain occurs in the channels’ axial direction inducing the overall change in the resistance. the real-time sensor behavior for different touch motions of the hand corroborates the theoretical model.

关键词： Deformable models Deformation Robot sensing systems Mathematical models Skin Real-time systems Microfluidics Strain Microchannels Immune system

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Review on Energy Efficient Wireless sensor Network Protocols 21

Review on Energy Efficient Wireless Sensor Network Protocols

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21st ieee international conference on Environment and Electrical Engineering / 5th ieee Industrial and Commercial Power systems Europe (EEEIC/I and CPS Europe)

作者： Ravinder, M. Kulkarni, Vikram MPSTME NMIMS Dept Informat Technol Mumbai Maharashtra India

ISBN: (纸本)9781665436137

Modern trends in advanced data communications lead to many improvements in the wireless sensor network (WSN) deployment. Sensing the data from the different environments is necessary to monitor and control the applications. It needs to organize a sufficient number of sensor nodes. In WSN, the small-sized sensor node is working with a very small battery with limited energy. Replacing the battery or recharging the battery is not feasible. So, the energy-efficient operation of WSN is the key factor. Many researchers have proposed WSN protocols for enhancing the lifetime and optimize the energy of WSN. In this paper, the performance of energy-efficient Algorithms like LEACH, PEGASIS, SEP, DDEEC, EESAA, IAATPC and EESCA are compared. Based on the simulation carried in MATLAB, the stability period of EESCA is enhanced by approximately 45%, 50% and network lifetime 44%, 65% as compared to LEACH and PEGASIS protocols respectively.

关键词： Wireless sensor network Energy Efficiency Sensing Coverage Radio Coverage

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CSS: Built-In Channel State Scrambling for Secure Wi-Fi Based Sensing

CSS: Built-In Channel State Scrambling for Secure Wi-Fi Base...

引用

international conference on distributed computing systems

作者： Xiao Deng Dongyu Xia Xun Wang Shuyu Shi Wei Wang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

this paper proposes CSS, a built-in channel state scrambling scheme to provide always-on protection for channel state information. CSS uses randomly generated scrambling vectors to emulate human activities, preventing eavesdroppers from recovering human physical activity from the channel state. By carefully designing the scrambling scheme based on physical channel models, we ensure that legacy receivers can successfully decode the frames and all frames transmitted over the air are scrambled. Furthermore, legitimate Wi-Fi sensors can still recover the true activity with a pre-shared secret key. Implementation on a real communication system shows that CSS can retain the same frame-error rate for commercial wireless receivers while misleading eavesdroppers with a success rate of over 95%.

关键词： Wireless communication Wireless sensor networks Receivers Vectors sensor systems OFDM modulation sensors

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Malware Detection By distributed systems with Partial Centralization

Malware Detection By Distributed Systems with Partial Centra...

引用

ieee international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications

作者： Bohdan Savenko Antonina Kashtalian Sergii Lysenko Oleg Savenko Khmelnytskyi National University Khmelnytskyi Ukraine

the paper proposes approach to use new tools for malware detection in corporate networks, which are distributed systems with partial centralization. To make decision about malware presence, the components which includes specified decision-making for the system's center are defined as a decentralized subsystem. To determine the states of the system components, characteristic indicators are proposed, and generalized analytical expressions for their calculation are developed. Such calculations make it possible to assess the state of the components in the system in order to determine its further steps. As a result, the system is the basis for usage of different malware detection methods in combination with the system components as an integral sensor. To test the system, a worm-virus detection method was implemented and experiments were conducted. the results of experimental studies approved the efficiency of the proposed solution.

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SimE4KG: distributed and Explainable Multi-Modal Semantic Similarity Estimation for Knowledge Graphs 5

SimE4KG: Distributed and Explainable Multi-Modal Semantic Si...

引用

5th ieee international conference on Artificial Intelligence and Knowledge Engineering, AIKE 2022

作者： Draschner, Carsten Felix Jabeen, Hajira Lehmann, Jens Information Systems and Artificial Intelligence University of Bonn Bonn Germany Big Data Analytics Gesis Leibniz Institute for the Social Sciences Cologne Germany Dresden Germany

ISBN: (数字)9781665471206

ISBN: (纸本)9781665471206

In recent years, more and more exciting sources of data have been modeled as Knowledge Graphs (KGs). this modeling represents both structural relationships and the entity specific multi-modal data in KGs. In various data analytic pipelines and Machine Learning (ML), the task of semantic similarity estimation plays a significant role. Assigning similarity values to entity pairs is needed in recommendation systems, clustering, classification, entity matching/disambiguation and many others. Efficient and scalable frameworks are needed to handle the quadratic complexity of all pair semantic similarity on Big Data KGs. Moreover, heterogeneous KGs demand multi-modal semantic similarity estimation to cover the versatile content like categorical relations between classes or their attribute literals like strings, timestamp or numeric data. In this paper we propose SimE4KG framework as a resource providing generic open source modules that compute semantic similarity estimation in multi-modal KGs. To justify the computational costs of similarity estimation, the SimE4KG generates reproducible, reusable and explainable results. the pipeline results are a native semantic RDF-KG, including the experiment results, hyper-parameter setup, and explanation of the results, like the most influential features. For fast and scalable execution in memory, we implemented the distributed approach using Apache Spark. the entire development of this framework is integrated into the holistic distributed semantic analytics stack SANSA. © 2022 ieee.

关键词： Machine learning

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Xorbits: Automating Operator Tiling for distributed Data Science 40

Xorbits: Automating Operator Tiling for Distributed Data Sci...

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40th ieee international conference on Data Engineering, ICDE 2024

作者： Lu, Weizheng He, Kaisheng Qin, Xuye Li, Chengjie Wang, Zhong Yuan, Tao Liao, Xia Zhang, Feng Chen, Yueguo Du, Xiaoyong Renmin University of China China Xorbits Inc China Communications Information Technology Group China Tsinghua University China

ISBN: (纸本)9798350317152

Data science pipelines commonly utilize dataframe and array operations for tasks such as data preprocessing, analysis, and machine learning. the most popular tools for these tasks are pandas and NumPy. However, these tools are limited to executing on a single node, making them unsuitable for processing large-scale data. Several systems have attempted to distribute data science applications to clusters while maintaining interfaces similar to single-node libraries, enabling data scientists to scale their workloads without significant effort. However, existing systems often struggle with processing large datasets due to Out-of-Memory (OOM) problems caused by poor data partitioning. To overcome these challenges, we develop Xorbits, a high-performance, scalable data science framework specifically designed to distribute data science workloads across clusters while retaining familiar APIs. the key differentiator of Xorbits is its ability to dynamically switch between graph construction and graph execution. Xorbits has been successfully deployed in production environments with up to 5k CPU cores. Its applications span various domains, including user behavior analysis and recommendation systems in the e-commerce sector, as well as credit assessment and risk management in the finance industry. Users can easily scale their data science workloads by simply changing the import line of their pandas and NumPy code. Our experiments demonstrate that Xorbits can effectively process very large datasets without encountering OOM or data-skewing problems. Over the fastest state-of-the-art solutions, Xorbits achieves an impressive 2.66 × speedup on average. In terms of API coverage, Xorbits attains a compatibility rate of 96.7%, surpassing the fastest framework by an impressive margin of 60 percentage points. Xorbits is available at https://***/xorbitsai/xorbits. © 2024 ieee.

关键词： Large datasets

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A Demonstration Platform for Large-Scaled Point Cloud Network Based on 28nm 2D/3D Unified Sparse Convolution Accelerator

A Demonstration Platform for Large-Scaled Point Cloud Networ...

引用

ieee international conference on Artificial Intelligence Circuits and systems (AICAS)

作者： Xiaoyu Feng Wenyu Sun Shupei Fan Chen Tang Yixiong Yang Jinshan Yue Qingmin Liao Huazhong Yang Yongpan Liu Tsinghua University Tsinghua Shenzhen International Graduated School Chinese Academy of Sciences

3D point cloud processing plays an important role in many emerging applications such as autonomous driving, visual navigation, and virtual reality. It calls for hardware acceleration of multiple key operations, including 3D Submanifold SCONV, 3D non-Submanifold SCONV, and 2D SCONV. this work presents a 2D/3D unified sparse convolution accelerator for large-scale voxel-based point cloud networks. the chip is fabricated in TSMC 28nm CMOS technology to achieve 3.3-16.9 FPS running from 60-400MHz when computing the SECOND network on KITTI dataset. this work has been included by ISSCC2023 [1]. A demonstration is given to show the real-time 3D processing with a lidar sensor.

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Detecting distributed Denial of Service Attacks in Internet of things Networks Using Machine Learning in Fog computing 5

Detecting Distributed Denial of Service Attacks in Internet ...

引用

5th international conference on Engineering Technology and its Applications, IICETA 2022

作者： Hassan, Karrar Falih Manaa, Mehdi Ebady University of Babylon College of Information Technology Babylon Iraq

ISBN: (纸本)9781665472159

the number of Internet of things (IoT) devices and the data produced by these devices have increased dramatically in recent years. IoT networks have benefited from the arrival of 5G, which aims to link more devices quickly and reliably. However, many of these devices are unsecured, leaving the Internet open to assault. Since IoT devices are resource-restricted, attackers may simply mount a larger and more complex DDoS assault using IoT devices. Entropy-based detection is technique proposed by other researchers, and it has a high sensitivity and a good degree of reliability. In this paper, authors propose an entropy-based framework with the machine learning algorithms to optimize the detection result. the proposed system consists of three parts: the first part is to create a sliding time window to calculate the entropy, and the second is using the Euclidean distance to make early detection possible during the DDoS but not after the collapse. the third part is the use of machine learning algorithms (KNN and SVM), to improve the accuracy of the results in early detection. the results showed that the proposed system is able to increase the accuracy from 98.6% to 100%, and efficiently detect attacks in real-time for (ICMP, TCP, UDP, HTTP and DNS) DDoS attacks. © 2022 ieee.

关键词： Internet of things

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Realtime Object Recognition Method Inspired by Multimodal Information Processing in the Brain for distributed Digital Twin systems 18

Realtime Object Recognition Method Inspired by Multimodal In...

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18th ieee international Wireless Communications and Mobile computing conference (IWCMC)

作者： Seki, Ryoga Kominami, Daichi Shimonishi, Hideyuki Murata, Masayuki Fujiwaka, Masaya Osaka Univ Grad Sch Informat Sci & Technol Osaka Japan NEC Corp Ltd Tokyo Japan NEC Corp Ltd Syst Platform Res Labs Yokohama Kanagawa Japan

ISBN: (纸本)9781665467490

Recently, digital twins have been paid much attention as a major application towards Beyond 5G/6G network, and real-time object recognition methods are key technology to digitize the real world as a digital twin. However, it is challenging to make a fast and accurate decision on what the object is from real-time streaming information such as video because accurate object recognition algorithms require a huge computation. To satisfy delay requirement of digital twin applications, such computations have to be moved from cloud to edges or even small terminal devices, where computing capacity is very limited. thus, recognition mechanisms have to be simplified for small devices but they would result in degraded accuracy. In this paper, we focus on the multimodal information processing mechanism of the brain, which makes decisions based on multiple types of uncertain observed information, to improve accuracy of simplified recognition mechanisms. We first propose a unimodal object recognition mechanism based on the Bayesian attractor model, which continuously recognizes objects from noisy streaming media data. then, we extend the mechanism with Bayesian causal inference to fuse the results of unimodal media recognition. through computer simulations, we show that our proposed method identifies an object accurately and quickly from uncertain observed information.

关键词： Mobile AR digital twin multimodal recognition Bayesian attractor model Bayesian causal inference

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