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Academic literature on the topic 'Adaptive Sharpness Aware Minimization'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Adaptive Sharpness Aware Minimization"

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Dung Nguyen, Duc, and Jae Wook Jeon. "Enhancing accuracy and sharpness of motion field with adaptive scheme and occlusion-aware filter." IET Image Processing 7, no. 2 (March 1, 2013): 144–53. http://dx.doi.org/10.1049/iet-ipr.2012.0563.

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Guo, Li, Weilong Chen, Yu Liao, Honghua Liao, and Jun Li. "An Edge-Preserved Image Denoising Algorithm Based on Local Adaptive Regularization." Journal of Sensors 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/2019569.

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Image denoising methods are often based on the minimization of an appropriately defined energy function. Many gradient dependent energy functions, such as Potts model and total variation denoising, regard image as piecewise constant function. In these methods, some important information such as edge sharpness and location is well preserved, but some detailed image feature like texture is often compromised in the process of denoising. For this reason, an image denoising method based on local adaptive regularization is proposed in this paper, which can adaptively adjust denoising degree of noisy image by adding spatial variable fidelity term, so as to better preserve fine scale features of image. Experimental results show that the proposed denoising method can achieve state-of-the-art subjective visual effect, and the signal-noise-ratio (SNR) is also objectively improved by 0.3–0.6 dB.
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Raj, Anish, Fabian Tollens, Laura Hansen, Alena-Kathrin Golla, Lothar R. Schad, Dominik Nörenberg, and Frank G. Zöllner. "Deep Learning-Based Total Kidney Volume Segmentation in Autosomal Dominant Polycystic Kidney Disease Using Attention, Cosine Loss, and Sharpness Aware Minimization." Diagnostics 12, no. 5 (May 7, 2022): 1159. http://dx.doi.org/10.3390/diagnostics12051159.

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Early detection of the autosomal dominant polycystic kidney disease (ADPKD) is crucial as it is one of the most common causes of end-stage renal disease (ESRD) and kidney failure. The total kidney volume (TKV) can be used as a biomarker to quantify disease progression. The TKV calculation requires accurate delineation of kidney volumes, which is usually performed manually by an expert physician. However, this is time-consuming and automated segmentation is warranted. Furthermore, the scarcity of large annotated datasets hinders the development of deep learning solutions. In this work, we address this problem by implementing three attention mechanisms into the U-Net to improve TKV estimation. Additionally, we implement a cosine loss function that works well on image classification tasks with small datasets. Lastly, we apply a technique called sharpness aware minimization (SAM) that helps improve the generalizability of networks. Our results show significant improvements (p-value < 0.05) over the reference kidney segmentation U-Net. We show that the attention mechanisms and/or the cosine loss with SAM can achieve a dice score (DSC) of 0.918, a mean symmetric surface distance (MSSD) of 1.20 mm with the mean TKV difference of −1.72%, and R2 of 0.96 while using only 100 MRI datasets for training and testing. Furthermore, we tested four ensembles and obtained improvements over the best individual network, achieving a DSC and MSSD of 0.922 and 1.09 mm, respectively.
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Vishal, Puri, and Ramesh Babu A. "Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm." International Journal of Artificial Life Research 8, no. 2 (July 2018): 1–24. http://dx.doi.org/10.4018/ijalr.2018070101.

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Wireless sensor networks (WSNs) are generally a group of spatially scattered and devoted sensors to record and monitor the physical environmental condition, and the collected data is grouped at a central location. In fact, the environmental conditions such as sound, humidity, temperature, wind, pollution levels, etc., can be clearly determined by WSNs. The principal objective of WSNs is to organize the whole sensor nodes in their related positions, thereby developing an effective network. In WSNs, target COVerage (TCOV) and Network CONnectivity (NCON) are the main concern of the sensor deployment problem. Many research works aspire the evolvement of smart context awareness algorithm for sensor deployment issues in WSN. Here the TCOV and NCON process are deployed as the minimization problem. This article makes an analysis of different GA variations in attaining the objective. The GA variations are as follows: self-adaptive genetic algorithm (SAGA), deterministic-adaptive genetic algorithm (DAGA), Individual- Adaptive Genetic Algorithm (IAGA). Finally, the methods are compared to one another in terms of connectivity and coverage performance.
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Bruschi, Roberto, Alessandro Carrega, and Franco Davoli. "A Game for Energy-Aware Allocation of Virtualized Network Functions." Journal of Electrical and Computer Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4067186.

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Network Functions Virtualization (NFV) is a network architecture concept where network functionality is virtualized and separated into multiple building blocks that may connect or be chained together to implement the required services. The main advantages consist of an increase in network flexibility and scalability. Indeed, each part of the service chain can be allocated and reallocated at runtime depending on demand. In this paper, we present and evaluate an energy-aware Game-Theory-based solution for resource allocation of Virtualized Network Functions (VNFs) within NFV environments. We consider each VNF as a player of the problem that competes for the physical network node capacity pool, seeking the minimization of individual cost functions. The physical network nodes dynamically adjust their processing capacity according to the incoming workload, by means of an Adaptive Rate (AR) strategy that aims at minimizing the product of energy consumption and processing delay. On the basis of the result of the nodes’ AR strategy, the VNFs’ resource sharing costs assume a polynomial form in the workflows, which admits a unique Nash Equilibrium (NE). We examine the effect of different (unconstrained and constrained) forms of the nodes’ optimization problem on the equilibrium and compare the power consumption and delay achieved with energy-aware and non-energy-aware strategy profiles.
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AbdulAlim, Md Abdul, Yu Cheng Wu, and Wei Wang. "A Fuzzy Based Clustering Protocol for Energy-Efficient Wireless Sensor Networks." Advanced Materials Research 760-762 (September 2013): 685–90. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.685.

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Minimization of energy consumption is one of the most important research areas in Wireless Sensor Networks. Nowadays, the paradigms of computational intelligence (CI) are widely used in WSN, such as localization, clustering, energy aware routing, task scheduling, security, etc. Though many fuzzy based clustering techniques have been proposed earlier, many of them could not increase the total network life time in terms of LND (Last Node Dies) with comparing to LEACH. In this paper, a fuzzy logic based energy-aware dynamic clustering technique is proposed, which increases the network lifetime in terms of LND. Here, two inputs are given in the fuzzy inference system and a node is selected as a cluster head according to the fuzzy cost (output). The main advantage of this protocol is that the optimum number of cluster is formed in every round, which is almost impossible in LEACH (low-energy adaptive clustering hierarchy). Moreover, this protocol has less computational load and complexity. The simulation result demonstrates that this approach performs better than LEACH in terms of energy saving as well as network lifetime.
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Garg, Anchal, and Gurjinder Kaur. "Zone Head Selection Algorithm Based on Fuzzy Logic for Wireless Sensor Networks." Journal of University of Shanghai for Science and Technology 23, no. 10 (October 1, 2021): 29–37. http://dx.doi.org/10.51201/jusst/21/09706.

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Clustering extends energy resources, improves scalability and preserves communication bandwidth of the network. Clustering is either categorized as static and dynamic or as equal and unequal. Hot-spots issue needs a high overhead and is prone to connectivity problems in the wireless sensor network and this can be only possible because of unequal clustering. In this paper a zone divisional method based on fuzzy logic has been proposed. This method uses a fuzzy logic to form clusters and allot nodes to them for the reduction of energy consumption, and extends the age of the sensor network. The simulation and results section shows that the outperformance of the proposed algorithm, where the (EAUCF) energy-aware unequal clustering fuzzy, (LEACH) lowenergy adaptive clustering hierarchy, (EAMMH) energy-aware multi-hop multi-path hierarchical, and (TTDFP) two-tier distributed fuzzy logic-based protocol for efficient data aggregation in multi-hop wireless sensor networks algorithms. The proposed algorithm has better results in terms of energy consumption minimization, load balancing, and prolongation of the network lifetime.
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Garg, Anchal, and Gurjinder Kaur. "Fuzzy Logic Based Zone Head Selection Algorithm for Wireless Sensor Networks." Journal of University of Shanghai for Science and Technology 23, no. 07 (July 24, 2021): 1210–15. http://dx.doi.org/10.51201/jusst/21/07294.

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Hot-spots are a problem that comes in the cluster-based routing protocol that employs multi-hop communication due to this problem the energy among the sensor nodes is not balanced. The hot-spots issue requires high overhead and is prone to connectivity issues in the sensor network this can be only possible because of unequal clustering. In this method, we have to act on all the nodes of the sensor network for communication. This process consumes high system energy if the numbers of nodes are very high. To offer guaranteed connectivity, decrease high usage and complexity, a fuzzy logic-based zone divisional method has been proposed in this paper. Use fuzzy logic to create clusters and assign nodes to them to decrease the consumption of energy and the age of the network prolongation. The simulation and results section shows the outperformance of the proposed protocol, where the (LEACH) low-energy adaptive clustering hierarchy, (EAUCF) energy-aware unequal clustering fuzzy,(EAMMH) energy-aware multi-hop multi-path hierarchical, and (TTDFP) two-tier distributed fuzzy logic-based protocol for efficient data aggregation in multi-hop wireless sensor networks algorithms. The proposed algorithm has better results in terms of energy consumption minimization, and prolongation of the network lifetime.
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Wu, Xintong, Shanlin Sun, Yun Li, Zhicheng Tan, Wentao Huang, and Xing Yao. "A Power Control Algorithm Based on Outage Probability Awareness in Vehicular Ad Hoc Networks." Advances in Multimedia 2018 (August 1, 2018): 1–8. http://dx.doi.org/10.1155/2018/8729645.

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This paper addresses the problem of adaptive power control based on outage probability minimization in Vehicular Ad Hoc Networks (VANETs), called a Power Control Algorithm Based on Outage Probability Awareness (PC-OPA). Unlike most of the existing works, our power control method aims at minimizing the outage probability and then is subject to the density of nodes in certain area. To fulfill power control, cumulative interference is assumed to be available at the transmitter of each terminal. The transmitters sent data by maximum power and then get the cumulative interference-aware outage probability. Furthermore, we build the interference model by stochastic geometric theory and then derive the expression between outage probability and cumulative interference. According to the expression, we adjust the transmitter power and optimize the outage probability. Simulation results are provided to demonstrate the effectiveness of the proposed power control strategies. It is shown that the PC-OPA can achieve a significant performance gain in terms of the outage probability and throughputs. Comparing MPC (Maximum Power Control algorithm) and WFPC (Water-Filled Power Control algorithm), the proposed PC-OPA decreased by 23% in terms of the outage probability and increased by 25% in terms of throughputs.
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Mueen, Ahmed, Mohammad Awedh, and Bassam Zafar. "Multi-obstacle aware smart navigation system for visually impaired people in fog connected IoT-cloud environment." Health Informatics Journal 28, no. 3 (July 2022): 146045822211126. http://dx.doi.org/10.1177/14604582221112609.

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Design of smart navigation for visually impaired/blind people is a hindering task. Existing researchers analyzed it in either indoor or outdoor environment and also it’s failed to focus on optimum route selection, latency minimization and multi-obstacle presence. In order to overcome these challenges and to provide precise assistance to visually impaired people, this paper proposes smart navigation system for visually impaired people based on both image and sensor outputs of the smart wearable. The proposed approach involves the upcoming processes: (i) the input query of the visually impaired people (users) is improved by the query processor in order to achieve accurate assistance. (ii) The safest route from source to destination is provided by implementing Environment aware Bald Eagle Search Optimization algorithm in which multiple routes are identified and classified into three different classes from which the safest route is suggested to the users. (iii) The concept of fog computing is leveraged and the optimal fog node is selected in order to minimize the latency. The fog node selection is executed by using Nearest Grey Absolute Decision Making Algorithm based on multiple parameters. (iv) The retrieval of relevant information is performed by means of computing Euclidean distance between the reference and database information. (v) The multi-obstacle detection is carried out by YOLOv3 Tiny in which both the static and dynamic obstacles are classified into small, medium and large obstacles. (vi) The decision upon navigation is provided by implementing Adaptive Asynchronous Advantage Actor-Critic (A3C) algorithm based on fusion of both image and sensor outputs. (vii) Management of heterogeneous is carried out by predicting and pruning the fault data in the sensor output by minimum distance based extended kalman filter for better accuracy and clustering the similar information by implementing Spatial-Temporal Optics Clustering Algorithm to reduce complexity. The proposed model is implemented in NS 3.26 and the results proved that it outperforms other existing works in terms of obstacle detection and task completion time.
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Conference papers on the topic "Adaptive Sharpness Aware Minimization"

1

Liu, Yong, Siqi Mai, Xiangning Chen, Cho-Jui Hsieh, and Yang You. "Towards Efficient and Scalable Sharpness-Aware Minimization." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01204.

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Bahri, Dara, Hossein Mobahi, and Yi Tay. "Sharpness-Aware Minimization Improves Language Model Generalization." In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-long.508.

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Li, Chen, Xin Luo, Wei Chen, Yulin He, Mingfei Wu, and Yusong Tan. "AttENT: Domain-Adaptive Medical Image Segmentation via Attention-Aware Translation and Adversarial Entropy Minimization." In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021. http://dx.doi.org/10.1109/bibm52615.2021.9669620.

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Yfoulis, Christos A., Anastasios Gounaris, and Dimitris Tzolas. "Minimization of the response time in parallel database queries: An adaptive cost-aware MPC-based solution." In Automation (MED 2011). IEEE, 2011. http://dx.doi.org/10.1109/med.2011.5983031.

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Sharma, Sandeep, and Jitendra Sonkar. "Energy minimization using NP-completeness Lower Energy Adaptive Aware Sink Relocation (NP-LEASR) in wireless sensor network." In The International Conference on Communication and Computing Systems (ICCCS-2016). Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315364094-46.

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