Sensor node faults are a serious threat to wireless sensor networks. They can cause node crashes or lead to the transmission of corrupted data. Especially the latter endangers the quality of subsequent data analyses. ...
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ISBN:
(纸本)9798350329100
Sensor node faults are a serious threat to wireless sensor networks. They can cause node crashes or lead to the transmission of corrupted data. Especially the latter endangers the quality of subsequent data analyses. Most related fault detection approaches consider the sensor nodes as black boxes. They neglect vital information available on the node level. Consequently, most of these approaches can not distinguish between (i) irregular but correctly sensed data events and (ii) data corruption caused by soft faults. In contrast, our contribution integrates node-level diagnostics with the characteristics of the sensor data. We utilize this node-level diagnostic information to present our fault detection approach. Based on simulations and practical experiments, we show the correctness and efficiency of our approach. The results show that our approach offers a high fault detection rate and can differentiate between events and faults. Furthermore, it consumes a justifiably small overhead of resources and energy.
Matrix multiplication is an essential tool in various engineering sciences. Matrices, as two-dimensional arrays, are excellent for representing the properties of complex systems. Multiplication of these matrices can t...
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In the era of big data, the escalating volume and velocity of data generation pose significant challenges in data processing. Traditional systems like Spark [1] and Hadoop [2] manage the increasing amount and velocity...
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Automatic segmentation of organs and regions in medical imaging is a valuable tool for specialists. This study explores various automatic methods for lung segmentation in X-ray images. First, various neural network ar...
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ISBN:
(纸本)9798350384734;9798350384727
Automatic segmentation of organs and regions in medical imaging is a valuable tool for specialists. This study explores various automatic methods for lung segmentation in X-ray images. First, various neural network architectures are applied for this segmentation task, and subsequently, they are ranked based on their performance through statistical analysis. Then, as some architectures are more suitable for segmenting certain structures or regions of the X-ray, aggregation and consensus methods are studied to fuse the various neural network segmentations, with the aim of obtaining a more complete segmentation. The study reveals that the method based on the WOWA aggregation function, coupled with a maximum-based consensus method, statistically outperforms the individual segmentation provided by the best-performing neural network.
Recently, the popularity of Internet of Things (IoT) devices has brought massive amounts of sensing data to edge networks. How to use distributed sensing data to train artificial intelligence (AI) models for ubiquitou...
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In recent years, there has been significant growth in mobile wireless sensor networks (WSNs), yet prevailing research has primarily focused on 2D planar deployments, overlooking the importance of three-dimensional (3D...
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In recent years, there has been significant growth in mobile wireless sensor networks (WSNs), yet prevailing research has primarily focused on 2D planar deployments, overlooking the importance of three-dimensional (3D) coverage in various applications. This oversight leads to ineffective data gathering due to incomplete area coverage and network connectivity. In previous researches (Boufares et al. in: 2018 31 IEEE/ACS 15th international conference on computersystems and applications (AICCSA), Aqaba, Jordan, pp 1-8, 2018, in: 13th international wireless communications and mobile computing conference (IWCMC), Valencia, pp 1628-1633, 2017, in: IEEE wireless communications and mobile computing conference (IWCMC), Dubrovnik, Croatia, pp 563-568, 2015a, in: the 4th international conference on performance evaluation and modeling in wired and wireless networks (PEMWN), Hammamet, Tunisia, pp 103-108, 2015b), we proposed 3D mobile autonomous redeployment strategies based on the Virtual Forces Algorithm, tailored for diverse configurations: 3D volume applications such as smart homes or agriculture, 3D flat surfaces like snow monitoring, and 3D terrain surfaces like volcano monitoring. Our approach ensured complete coverage and connectivity in these scenarios. Moreover, energy efficiency emerges as a critical concern, given the autonomous and mobile nature of sensor nodes operating on finite battery power. Hence, in this paper, we provide an overview of our previous results, highlighting the efficacy of our 3D mobile autonomous redeployment strategies across various configurations. Subsequently, we delve into an in-depth analysis of the energy consumption associated with the different proposed contributions. Building upon these insights, we propose an energy harvesting approach aimed at extending the operational lifespan of mobile 3D WSNs, thus ensuring sustained functionality in diverse real-world *** these contributions, we address critical challenges and p
Nowadays, due to the rapid development and application of deep learning, its application in computer simulation environment system has become a hot topic. The environment model building module in computer simulation e...
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This paper proposes a distributed estimation and control algorithm to allow a team of robots to search for and track an unknown number of targets. The number of targets in the area of interest varies over time as targ...
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This paper proposes a distributed estimation and control algorithm to allow a team of robots to search for and track an unknown number of targets. The number of targets in the area of interest varies over time as targets enter or leave, and there are many sources of sensing uncertainty, including false positive detections, false negative detections, and measurement noise. The robots use a novel distributed Multiple Hypothesis Tracker (MHT) to estimate both the number of targets and the states of each target. A key contribution is a new data association method that reallocates target tracks across the team. The distributed MHT is compared against another distributed multi-target tracker to test its utility for multi-robot, multi-target tracking.
Computation of the inner products is frequently used in machine learning (ML) algorithms apart from signal processing and communication applications. distributed arithmetic (DA) has been frequently employed for area-t...
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ISBN:
(纸本)9798350330991;9798350331004
Computation of the inner products is frequently used in machine learning (ML) algorithms apart from signal processing and communication applications. distributed arithmetic (DA) has been frequently employed for area-time efficient inner-product implementations. In conventional DA-based architectures, one of the vectors is constant and known a priori. Hence, the traditional DA architectures are not suitable when both vectors are variable. However, computing the inner product of a pair of variable vectors is frequently used for matrix multiplication of various forms and convolutional neural networks. In this paper, we present a novel DA-based architecture for computing the inner product of variable vectors. To derive the proposed architecture, the inner product of any given length is decomposed into a set of short-length inner products, such that the inner product could be computed by successive accumulation of the results of shortlength inner products. We have designed a DA-based architecture for the computation of the short-length inner-product of variable vectors and used that in successive clock cycles to compute the whole inner-product by successive accumulation. The post-layout synthesis results using Cadence Innovus with a GPDK 90nm technology library show that the proposed DA-based parallel architecture offers significant advantages in area-delay product and energy consumption over the bit-serial DA architecture.
Many recent pattern recognition applications rely on complex distributed architectures in which sensing and computational nodes interact together through a communication network. Deep neural networks (DNNs) play an im...
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ISBN:
(纸本)9798350332773
Many recent pattern recognition applications rely on complex distributed architectures in which sensing and computational nodes interact together through a communication network. Deep neural networks (DNNs) play an important role in this scenario, furnishing powerful decision mechanisms, at the price of a high computational effort. Consequently, powerful state-of-the-art DNNs are frequently split over various computational nodes, e.g., a first part stays on an embedded device and the rest on a server. Deciding where to split a DNN is a challenge in itself, making the design of deep learning applications even more complicated. Therefore, we propose Split-Et-Impera, a novel and practical framework that i) determines the set of the best-split points of a neural network based on deep network interpretability principles without performing a tedious try-and-test approach, ii) performs a communication-aware simulation for the rapid evaluation of different neural network rearrangements, and iii) suggests the best match between the quality of service requirements of the application and the performance in terms of accuracy and latency time.
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