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An energy-efficient fuzzy based data fusion and tree based clustering algorithm for wireless sensor networks 3rd

An energy-efficient fuzzy based data fusion and tree based c...

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3rd International Symposium on Intelligent Systems Technologies and applications, ISTA 2017

作者： Venkatesh, Veeramuthu Raj, Pethuru Balakrishnan, P. School of Computing SASTRA University Thanjavur613401 India Bangalore560025 India SCOPE Department of Analytics VIT University Vellore Campus Vellore India

ISBN: (纸本)9783319683843

The realization of any wireless sensor network (WSNs) clearly determined on how the key quality of service (QoS) attributes/non-functional requirements (NFRs) gets accomplished. The well-known issues to be taken into account while designing WSNs for setting up smarter environments are information precision (timeliness and accuracy), data accretion, network latency, and energy efficiency. A wisely employed clustering algorithm for interconnecting sensor nodes can significantly enhance the energy efficiency of WSNs. However, the aspect of clustering involves additional overheads due to cluster head selection and cluster construction. This research work proposes a workaround that utilizes Type-ii fuzzy for fusing the data and tree driven clustering algorithm that employs Type-2 fuzzy logic to improve the QoS parameters as well as preserving the power/energy of sensor networks. The primary objectives of the proposed cluster algorithm are two folded. Firstly, it constructs the clusters and chooses the cluster head (CH) by considering the remaining energy in the nodes and its distance from the base station (BS). Secondly, it performs the data fusion which contains meaningful information that has been sensed and captured. An extensive experimental analysis has been done on the proposed FBDF-TBC method by comparing it against its counterparts. The simulation results conclude that the proposed fuzzy-based technique for data fusion and tree-based clustering routing algorithm (FBDF-TBC) outperforms other clustering algorithms and improves the overall network lifetime of WSN from a minimum of 16% to maximum of 76%. © Springer International Publishing AG 2018.

关键词： Clustering algorithms

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A novel approach to guarantee causal message ordering in pre-planned wireless sensor networks

A novel approach to guarantee causal message ordering in pre...

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12th International Conference on algorithms and architectures for Parallel Processing, ICA3PP 2012

作者： Kim, Chayoung Ahn, Jinho Dept. of Computer Science College of Natural Science Kyonggi University Suwon Gyeonggi-do 443-760 Korea Republic of

ISBN: (纸本)9783642330643

Data fusion often depends on the time of occurrence of fused sensor readings, called as temporal ordering, which is defined with respect to a single message, or as causal ordering to guarantee dependency relationships between messages in many-to-many communication patterns. There are some temporal ordering protocols based on physical time synchronization in publish/subscribe (P/S) paradigm of wireless sensor networks, but there exist little research works on development of causal ordering protocols based on logical time in P/S of wireless sensor networks (WSNs). Causal message ordering is more useful for most distributed applications in which a large number of sensor nodes request cooperating to fuse their data in WSNs. Temporal ordering is not sufficient for these distributed applications because it is not defined for dependency relationships between these messages. Also, many-to-many communication patterns attempt to address the problem of providing scalability of data propagation, guaranteeing message delivery order and supporting overlapping multicast groups in WSNs. In this paper, we present a novel approach based on gossiping and firefly synchronization instead of physical time synchronization, guaranteeing the causal message ordering property in P/S of WSNs. In the proposed protocol, every sensor broker disseminates the multicast message including the latest time-stamped information that represents the gossip round in which the message is generated to subscribers for causal message ordering. The latest time-stamped information is represented using colors. Its scalability feature might be highly suitable for the area of the applications requiring only the minimum causal information of message delivery with flexible consistency by cooperating to fuse their data. © 2012 Springer-Verlag.

关键词： Scalability

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Bio-fusion for intelligent system control

Bio-fusion for intelligent system control

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Proceedings of the 1999 sensor fusion: architectures, algorithms, and applications iiI

作者： Norseen, John D. Kropotov, Juri D. Kremen, Inna Z. Lockheed Martin Aeronautical Co

We present a realistic neural network - the canonical cortical module - built on basic principles of cortical organization. These principles are: opponent cells principle, assembly principle, canonical cortical circuit principle and modular principle. When applied to visual images, the network explains orientational and spatial frequency filtering functions of neurons in the striate cortex. Two patterns of joint distribution of opponent cells in the inhibitory cortical layer are presented: pinwheel and circular. These two patterns provide two Gestalt descriptions of local (within the frames of one module) visual image: circle-ness and cross-ness. These modules were shown to have a power for shape detection and texture discrimination. They also provide an enhancement of signal-to-noise ratio of input images. Being modality independent, the canonical cortical module seems to be good tool for bio-fusion for intelligent system control.

关键词： Neural networks

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Multisensor information compression and reconstruction

Multisensor information compression and reconstruction

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Multisensor, Multisource Information fusion: architectures, algorithms, and applications 2009

作者： Bing, Du Liang, Liu Jun, Zhang School of Electronics and Information Engineering Beihang Univ. XueYuan Road 37 Haidian District Beijing 100191 China

ISBN: (纸本)9780819476111

In this paper, we propose a method of sampled data compression and reconstruction using the theory of distributed compressed sensing for wireless sensor network, in which the correlation between the sensors is considered for joint sparsity representation, compression and reconstruction of the signals. And incoherent random projection CS matrix in each sensor is as encoding matrix to generate compressed measurements for storing, delivering and processing. The reconstruction algorithm with both acceptable complexity and precision is developed for noise corrupted measurements by fully utilizing of correlations diversity. The simulation shows that the number of measurements only slightly larger than the sparsity of the sampled sensor data is needed for successful recovery. © 2009 SPIE.

关键词： Wireless sensor networks

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Real-time fusion of image and inertial sensors for navigation

Real-time fusion of image and inertial sensors for navigatio...

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63rd Annual Meeting of the Institute of Navigation 2007

作者： Fletcher, J. Veth, M. Raquet, J. Air Force Institute of Technology United States Air Force Department of Electrical and Computer Engineering Air Force Institute of Technology Center

ISBN: (纸本)1604232862

As evidenced by many biological systems, the fusion of optical and inertial sensors represents an attractive method for passive navigation. In our previous work, a rigorous theory for optical and inertial fusion was developed for precision navigation applications. The theory was based on a statistical transformation of the feature space based on inertial sensor measurements. The transformation effectively constrained the feature correspondence search to a given level of a priori statistical uncertainty. When integrated into a navigation system, the fused system demonstrated performance in indoor environments which were comparable to that of GPS-aided systems. In order to improve feature tracking performance, a robust feature transformation algorithm (Lowe's SIFT) was chosen. The SIFT features are ideal for navigation applications in that they are invariant to scale, rotation, and illumination. Unfortunately, there exists a correlation between feature complexity and computer processing time. This limits the effectiveness of robust feature extraction algorithms for real-time applications using traditional microprocessor architectures. While recent advances in computer technology have made image processing more commonplace, the amount of information that can be processed is still limited by the power and speed of the CPU. In this paper, a new theory which exploits the highly parallel nature of General Programmable Graphical Processing Units (GPGPU) is developed which supports deeply integrated optical and inertial sensors for real-time navigation. Recent advances in GPGPU technology have made realtime, image-aided navigation a reality. Our approach leverages the existing OpenVIDIA core GPGPU library and commercially available computer hardware to solve the image and inertial fusion problem. The open-source libraries are extended to include the statistical feature projection and matching techniques developed in our previous research. The performance of the new processin

关键词： Inertial navigation systems

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Advancements in Object Detection for Unmanned Aerial Vehicles: applications, Challenges, and Future Perspectives 12

Advancements in Object Detection for Unmanned Aerial Vehicle...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Sezgin, Anil Boyaci, Aytug Siemens Corporate Technology Istanbul Turkey National Defence University Air Force Academy Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350330366

Unmanned Aerial Vehicles (UAVs) are used in different fields ranging from recreational vehicles to agriculture, environmental monitoring, infrastructure inspection, disaster management, security, surveillance and logistics. This paper provides an overview of UAV applications and highlights the importance of object detection in improving drone autonomy. Object detection facilitates tasks such as precision agriculture, disaster response, environmental protection, infrastructure inspection and autonomous navigation. The paper reviews recent advances in UAV-specific object detection algorithms and methodologies and highlights challenges such as varying altitudes, motion blur, real-time processing constraints and limited computational resources. Solutions and adaptations are discussed to overcome these challenges, including lightweight neural network architectures, transfer learning, data augmentation, edge computing, multi-sensor fusion, attention mechanisms and adaptive algorithms. The study highlights the importance of integrating object detection systems into UAVs to improve their use in various sectors and contribute to improvements in surveillance missions. © 2024 IEEE.

关键词： Object detection

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The Effects of Movement Speeds and Magnetic Disturbance on Inertial Measurement Unit Accuracy: The Implications of sensor fusion algorithms in Occupational Ergonomics applications

The Effects of Movement Speeds and Magnetic Disturbance on I...

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作者： Chen, Howard The University of Iowa

学位级别：Ph.D.

Accurate risk assessment tools and methods are necessary to understand the relationship between occupational exposure to physical risk factors and musculoskeletal disorders. Ergonomists typically consider direct measurement methods to be the most objective and accurate of the available tools. However, direct measurement methods are often not used due to cost, practicality, and worker/workplace disruption. Inertial measurement units (IMUs), a relatively new direct measurement technology used to assess worker kinematics, are attractive to ergonomists due to their small size, low cost, and ability to reliably capture information across full working shifts. IMUs are often touted as a field-capable alternative to optical motion capture systems (OMCs). The error magnitudes of IMUs, however, can vary significantly (>15°) both within and across studies. The overall goals of this thesis were to (i) provide knowledge about the capabilities and limitations of IMUs in order to explain the inconsistencies observed in previous studies that assessed IMU accuracy, and (ii) provide guidance for the ergonomics community to leverage this technology. All three studies in this dissertation systematically evaluated IMUs using a repetitive material transfer task performed by thirteen participants with varying movement speeds (15, 30, 45 cycles/minute) and magnetic disturbance (absent, present). An OMC system was used as the reference device. This first study systematically evaluated the effects of motion speed and magnetic disturbance on the spatial orientation accuracy of an inertial measurement unit (IMU) worn on the hand. Root-mean-square differences (RMSD) exceeded 20° when inclination measurements (pitch and roll) were calculated using the IMU’s accelerometer. A linear Kalman filter and a proprietary, embedded Kalman filter reduced inclination RMSD to <3° across all movement speeds. The RMSD in the heading direction (i.e., about gravity) increased (from <5° to 17°) under magnetic dis

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Unlocking High Performance with Low-Bit NPUs and CPUs for Highly Optimized HPL-MxP on Cloud Brain ii 24

Unlocking High Performance with Low-Bit NPUs and CPUs for Hi...

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2024 International Conference for High Performance Computing, Networking, Storage and Analysis

作者： Xue, Weicheng Yang, Kai Liu, Yongxiang Fan, Dengdong Xu, Pengxiang Tian, Yonghong Pengcheng Lab Shenzhen 518055 Guangdong Peoples R China

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

Mix-precision computation is crucial for artificial intelligence and scientific computing applications. However, as novel chips with innovative architectures emerge, harnessing their computational capabilities presents significant challenges. While existing algorithms for the HPL-MxP LU factorization excel on homogeneous systems, they often encounter difficulties on specialized heterogeneous architectures. This deficiency arises from inadequate optimization for computation, memory access, and communication, hindering effective mixed-precision acceleration. This work introduces an algorithm-hardware co-optimization approach for LU factorization on specialized NPUs and CPUs, leveraging their unique architectures. A novel multi-iteration fusion method for general matrix multiplication is proposed, strategically designed to maximize on-chip L1 buffer utilization, effectively overcoming the notorious "memory wall". Additionally, a multi-stage, multi-level heterogeneous pipeline for LU factorization in an accelerator-CPU cloud environment is presented, where compute-intensive matrix multiplications are offloaded to NPUs while CPUs handle the remaining tasks. The co-optimization approach fosters deep collaboration between CPUs and accelerators, thereby unlocking enhanced performance.

关键词： Scientific computing High performance computing Pipelines AI accelerators Collaboration Computer architecture Heterogeneous networks System-on-chip Optimization

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Multiple IMU fusion Algorithm Comparison for Sounding Rocket Attitude applications 35

Multiple IMU Fusion Algorithm Comparison for Sounding Rocket...

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35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022

作者： Gómez, M.A. García, Laura Train Rico, Alberto Solera Gómez-Pérez, Ignacio Sánchez, Eusebio Valero Universidad Politécnica de Madrid Spain Universidad Carlos III de Madrid Spain Instituto Nacional de Técnica Aeroespacial Spain

ISBN: (纸本)9781713871361

Micro-electro-mechanical (MEMS) technology is known to offer a wide variety of advantages for navigation and guidance purposes in terms of cost, size and weight. However, sensor accuracy limits its applications. The use of a Redundant Inertial Measurement Unit (RIMU) configuration allows combining inertial observations not only to obtain a better performances for attitude estimation, but also to be able to detect and isolate a sensor not properly working. The output signals of uncorrelated IMU sensors can be integrated using a data fusion algorithm (e.g Extended Kalman Filter, EKF). The aim of this study is to present the implementation of several filters for an array of consumer grade IMUs placed on a "skew-redundant" configuration in a sounding rocket vehicle. The filtering method allows designing an attitude estimator capable of improving the kinematic predictions from gyroscope measurement integration by applying a correction method from accelerometer and magnetometer data. Moreover, simulations are performed to analyze the resilience and fault detection, while laboratory tests check the performance of the two proposed architectures. With the aim of testing accuracy of the algorithms for the RIMU a software simulation tool and laboratory tests are presented. © 2022 35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022. All rights reserved.

关键词： Extended Kalman filters

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Performance evaluation on contour extraction using Hough transform and RANSAC for multi-sensor data fusion applications in industrial food inspection

Performance evaluation on contour extraction using Hough tra...

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IEEE Signal Processing algorithms, architectures, Arrangements, and applications Conference

作者： M. Kroger W. Sauer-Greff R. Urbansky M. Lorang M. Siegrist Chair for Communications Engeneering University of Kaiserslautern WIPOTEC GmbH

ISBN: (纸本)9781509026616

For multi-sensor data fusion applications the accurate alignment of different sensor data is essential for the proper combination of matching features. In food inspection system the boxing often is in a rectangular shape. This knowledge can be used to rectify the image data, an important step in the alignment stage. In case of low contrast between boxing and background, the detected contour may differ significantly from the actual values. In this paper the performance of the Hough transform and the RANdom SAmple Consensus (RANSAC)-algorithm are evaluated relating to the correct extraction of the boxing contour out of contour data distorted by position errors of the outer shape. The evaluation results indicate the superiority of the RANSAC algorithm with respect to scalability, robustness and execution time.

关键词： Hough transform Food inspection Edge detection boxing multi-sensor data fusion sensor Data Performance Evaluation location error contour image data execution time

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