A link-elimination partitioning approach for application graph mapping in reconfigurable computing systems

Mohtavipour, Seyed Mehdi; Shahhoseini, Hadi Shahriar

doi:10.1007/s11227-019-03056-5

Cited by 20 publications

(2 citation statements)

References 47 publications

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“…The fruit fly optimization algorithm had more accuracy and faster convergence speed compared to other algorithms. To detect high-degree nodes, distance model was used to implement resource graph in [ 77 ]. The Hessian regularization technique in iterative manner was used in [ 78 ] for localization of sensor nodes.…”

Section: Literature Reviewmentioning

confidence: 99%

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics

2022

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Wireless sensor networks (WSNs) contain sensor nodes in enormous amount to accumulate the information about the nearby surroundings, and this information is insignificant until the exact position from where data have been collected is revealed. Localization of sensor nodes in WSNs plays a significant role in several applications such as detecting the enemy movement in military applications. The major aim of localization problem is to find the coordinates of all target nodes with the help of anchor nodes. In this paper, two variants of bat optimization algorithm (BOA) are proposed to localize the sensor nodes in a more efficient way and to overcome the drawbacks of original BOA, i.e. being trapped in local optimum solution. The exploration and exploitation features of original BOA are modified in the proposed BOA variants 1 and 2 using improved global and local search strategies. To validate the efficiency of the proposed BOA variants 1 and 2, several simulations have been performed for various numbers of target nodes and anchor nodes, and the results are compared with original BOA and other existing optimization algorithms applied to node localization problem. The proposed BOA variants 1 and 2 outperform the other algorithms in terms of mean localization error, number of localized nodes and computing time. Further, the proposed BOA variants 1 and 2 and original BOA are also compared in terms of various errors and localization efficiency for several values of target and anchor nodes. The simulations results signify that the proposed BOA variant 2 is superior to the proposed BOA variant 1 and existing BOA in terms of several errors. The node localization based on the proposed BOA variant 2 is more effective as it takes less time to perform computations and has less mean localization error than the proposed BOA variant 1, BOA and other existing optimization algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics

2022

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“…Traditionally, farmers have relied on manual observation for monitoring chicken conditions, a time-consuming and error-prone task due to the sheer number of chickens. However, the decreasing cost of technological devices [2] and introduction of powerful Deep Neural Network (DNN) [3] have facilitated the widespread adoption of monitoring systems in commercial farms. Leveraging artificial intelligence (AI), these systems autonomously monitor chickens, offering farmers an efficient means to manage poultry houses and enhance profitability.…”

Section: Introductionmentioning

confidence: 99%

DABA-Net: Deep Acceleration-Based AutoEncoder Network for Violence Detection in Surveillance Cameras

Ehsan

Nahvi

Mohtavipour

2022

2022 International Conference on Machine Vision and Image Processing (MVIP)

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Detecting anomalies in poultry houses is crucial for maintaining optimal chicken health conditions, minimizing economic losses and bolstering profitability. This paper presents a novel real-time framework for analyzing chicken behavior in cage-free poultry houses to detect abnormal behaviors. Specifically, two significant abnormalities, namely inactive broiler and huddling behavior, are investigated in this study. The proposed framework comprises three key steps: (1) chicken detection utilizing a state-of-the-art deep learning model, (2) tracking individual chickens across consecutive frames with a fast tracker module, and (3) detecting abnormal behaviors within the video stream. Experimental studies are conducted to evaluate the efficacy of the proposed algorithm in accurately assessing chicken behavior. The results illustrate that our framework provides a precise and efficient solution for real-time anomaly detection, facilitating timely interventions to maintain chicken health and enhance overall productivity on poultry farms.

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Locality-aware virtual machine placement based on similarity properties in mobile edge computing

Mostafavi Amjad,

Eslamnour

2024

Cluster Comput

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A link-elimination partitioning approach for application graph mapping in reconfigurable computing systems

Cited by 20 publications

References 47 publications

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics

Localization of sensor nodes in wireless sensor networks using bat optimization algorithm with enhanced exploration and exploitation characteristics

DABA-Net: Deep Acceleration-Based AutoEncoder Network for Violence Detection in Surveillance Cameras

Locality-aware virtual machine placement based on similarity properties in mobile edge computing

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