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Soft Computing Techniques for Dependable Cyber-Physical Systems [article]

Muhammad Atif, Siddique Latif, Rizwan Ahmad, Adnan Khalid Kiani, Junaid Qadir, Adeel Baig, Hisao Ishibuchi, Waseem Abbas
2020 arXiv   pre-print
computation, probabilistic reasoning and rough sets.  ...  In this regard, soft computing is an emerging paradigm that can help to overcome the vulnerabilities, and unreliability of CPS by using techniques including fuzzy systems, neural network, evolutionary  ...  Artificial Neural Networks Based on their biological counterparts, Artificial Neural Networks (ANN) are massively parallel distributed systems for processing information.  ... 
arXiv:1801.10472v2 fatcat:2gsfqsmwgzevrchlpifxqmlyri

2020 Index IEEE Transactions on Signal Processing Vol. 68

2020 IEEE Transactions on Signal Processing  
., One-Step Prediction for Discrete Time-Varying Nonlinear Systems With Unknown Inputs and Correlated Noises; TSP  ...  ., +, TSP 2020 2670-2681 Group Sparsity Based Localization for Far-Field and Near-Field Sources Based on Distributed Sensor Array Networks.  ...  ., +, TSP 2020 3090-3102 Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network-Based Vector-to-Vector Regression.  ... 
doi:10.1109/tsp.2021.3055469 fatcat:6uswtuxm5ba6zahdwh5atxhcsy

The Role of Artificial Intelligence (AI) in Condition Monitoring and Diagnostic Engineering Management (COMADEM): A Literature Survey

B. K. Nagaraja Rao
2021 American Journal of Artificial Intelligence  
AI techniques such as, knowledge based systems, expert systems, artificial neural networks, genetic algorithms, fuzzy logic, casebased reasoning and any combination of these techniques (hybrid systems)  ...  , machine learning, biomimicry such as swarm intelligence and distributed intelligence. are widely used by multi-disciplinarians to solve a whole range of hitherto intractable problems associated with  ...  Wireless Sensor Network (WSN) is a network based on multiple lowcost, low-energy sensor nodes connected to physical signals.  ... 
doi:10.11648/j.ajai.20210501.12 fatcat:gvplqmpqubdw3pquik5eavux5u

Table of Contents

2020 IEEE Transactions on Signal Processing  
Ang 3400 Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network-Based Vector-to-Vector Regression .  ...  Mitra 3209 Guaranteed Recovery of One-Hidden-Layer Neural Networks via Cross Entropy .. . . . . . . . . . H. Fu, Y. Chi, and Y.  ... 
doi:10.1109/tsp.2020.3042287 fatcat:nh7viihaozhd7li3txtadnx5ui

The ISTI Rapid Response on Exploring Cloud Computing 2018 [article]

Carleton Coffrin, James Arnold, Stephan Eidenbenz, Derek Aberle, John Ambrosiano, Zachary Baker, Sara Brambilla, Michael Brown, K. Nolan Carter, Pinghan Chu, Patrick Conry, Keeley Costigan, Ariane Eberhardt (+31 others)
2019 arXiv   pre-print
These demonstrations ranged from deploying proprietary software in a cloud environment to leveraging established cloud-based analytics workflows for processing scientific datasets.  ...  However, training a complex deep neural network on a large dataset can take a significant amount of time. Therefore, accelerated training of neural network models is very important.  ...  Solution Approach We concentrated on the Amazon SageMaker machine learning platform for RF modulation classification based on a convolutional neural network (CNN).  ... 
arXiv:1901.01331v1 fatcat:cdkmje2agzfsdpyulbp4cxz22q

Machine Learning of Spatial Data

Behnam Nikparvar, Jean-Claude Thill
2021 ISPRS International Journal of Geo-Information  
Figure 12 . 12 Model complexity and regularization. (a) Overfitting: small lambda value. (b) Well-fitted: lambda value is tuned. (c) Underfitting: large lambda value.  ...  Deep neural networks based on a combination of LSTM and CNNs introduce simultaneous learning across space, time, scales, and hierarchies.  ... 
doi:10.3390/ijgi10090600 fatcat:fqvu4og76newree6pakfqgdbia

Resilient Cyberphysical Systems and their Application Drivers: A Technology Roadmap [article]

Somali Chaterji, Parinaz Naghizadeh, Muhammad Ashraful Alam, Saurabh Bagchi, Mung Chiang, David Corman, Brian Henz, Suman Jana, Na Li, Shaoshuai Mou, Meeko Oishi, Chunyi Peng (+5 others)
2019 arXiv   pre-print
In the paper, our focus is on design and deployment innovations that are broadly applicable across a range of CPS application areas.  ...  denial-of-service) attacks or other cybersecurity nightmares, so called "black swan" events, disabling critical services of the municipal electrical grids and other connected infrastructures, data breaches, and network  ...  Multi-hop dependency: Cascading failures can spread beyond one-hop dependencies as well.  ... 
arXiv:2001.00090v1 fatcat:zaybw5wyfbayhcu34q2douctme

Conference Guide [Front matter]

2020 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)  
Based on the barycentric coordinates of the tetrahedron, the expansion of the sensor network is realized through the multi-hop of the UWB sensor.  ...  TuCT4 Room 4 This paper proposes a multi-hop distributed sensor network localization method based on UWB ranging measurements.  ...  In this paper we investigate the resilient consensus problem for multi-agent systems under the specific attack scenarios where the attacker can eavesdrop on initial information of agents among the system  ... 
doi:10.1109/icarcv50220.2020.9305477 fatcat:4h7gpoj7ljgsrlkjoyw3qcfzxi

A Manifesto for Future Generation Cloud Computing

Rajkumar Buyya, Marco A. S. Netto, Adel Nadjaran Toosi, Maria Alejandra Rodriguez, Ignacio M. Llorente, Sabrina De Capitani Di Vimercati, Pierangela Samarati, Dejan Milojicic, Carlos Varela, Rami Bahsoon, Marcos Dias De Assuncao, Satish Narayana Srirama (+13 others)
2018 ACM Computing Surveys  
ACKNOWLEDGMENTS We thank anonymous reviewers, Sartaj Sahni (Editor-in-Chief) and Antonio Corradi (Associate Editor) for their constructive suggestions and guidance on improving the content and quality  ...  on their cost-awareness and the constraint requirements of tasks; 5) multi-Cloud load balancers, to spread the load of an application across multiple CDCs.  ...  Convolutional Neural Networks (CNNs), Multi-Layer Perceptrons (MLPs), and Long Short-Term Memory (LSTMs)-data stream analytics, and image and video pattern recognition.  ... 
doi:10.1145/3241737 fatcat:bgb4qjtm5zgcbbtyy6x6anfeju

A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade [article]

Rajkumar Buyya, Satish Narayana Srirama, Giuliano Casale, Rodrigo Calheiros, Yogesh Simmhan, Blesson Varghese, Erol Gelenbe, Bahman Javadi, Luis Miguel Vaquero, Marco A. S. Netto, Adel Nadjaran Toosi, Maria Alejandra Rodriguez, Ignacio M. Llorente (+12 others)
2018 arXiv   pre-print
Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model.  ...  The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud  ...  Acknowledgement We thank anonymous reviewers, Sartaj Sahni (Editor-in-Chief) and Antonio Corradi (Associate Editor) for their constructive suggestions and guidance on improving the content and quality  ... 
arXiv:1711.09123v2 fatcat:xu3u75e6ind23ite3l6zfqi6hq

Defense Advanced Research Projects Agency (Darpa) Fiscal Year 2015 Budget Estimates

Department Of Defense Comptroller's Office
2014 Zenodo  
DARPA seeks to improve the analysis of large neural data sets by creating interfaces that will allow researchers to generate new models across multiple scales.  ...  warriors with post-traumatic brain injury, stress, or loss of memory (i.e. the Restoring Active Memory (RAM) funding opportunity), as well as new neurotechnology-based capabilities (e.g. the Systems-Based  ...  AWARE aggregated the following programs: Lambda Scale, Broadband, Multi-Band and Wide Field of View.  ... 
doi:10.5281/zenodo.1215345 fatcat:fjzhmynqjbaafk67q2ckcblj2m

Drone Deep Reinforcement Learning: A Review

Ahmad Taher Azar, Anis Koubaa, Nada Ali Mohamed, Habiba A. Ibrahim, Zahra Fathy Ibrahim, Muhammad Kazim, Adel Ammar, Bilel Benjdira, Alaa M. Khamis, Ibrahim A. Hameed, Gabriella Casalino
2021 Electronics  
In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques.  ...  Tai and Liu's studies first developed a model-free Deep Reinforcement Learning (DRL) algorithm based on a convolutional neural network [62] .  ...  There are two branches of RL algorithms: model-based and model-free; model-based RL algorithms try to choose the optimal policy based on the learned model of the environment, while in model-free algorithms  ... 
doi:10.3390/electronics10090999 doaj:57ededb7d1a0445eaf34975cb6625c1f fatcat:kya3fbblszd27i4exlybnji4ni

Toward a generalized theory comprising digital, neuromorphic, and unconventional computing

Herbert Jaeger
2021 Neuromorphic Computing and Engineering  
The main contribution of this paper is an in-depth inspection of existing formal conceptualizations of 'computing' in discrete-symbolic, probabilistic and dynamical-systems oriented views.  ...  It is fully understood how this translates into transistor-and-wire based digital hardware architectures, and the corresponding microchip design and manufacturing technologies have reached astounding degrees  ...  lambda operator in lambda calculus, or by constructing parse trees in grammar-based formalisms.  ... 
doi:10.1088/2634-4386/abf151 fatcat:w5p4uup3r5avxiqh2tecyeti6i

Edge-Oriented Computing: A Survey on Research and Use Cases

Nour Alhuda Sulieman, Lorenzo Ricciardi Celsi, Wei Li, Albert Zomaya, Massimo Villari
2022 Energies  
networks.  ...  the century of data due to the rapid increase in the quantity of exchanged data worldwide (especially in smart city applications such as autonomous vehicles), collecting and processing such data from sensors  ...  on big data and multi-sensor platforms-for further detail in this respect, the interested reader is referred to the solution proposed in [43] .  ... 
doi:10.3390/en15020452 fatcat:eyz6iiselbar5cokm22eldpxam

Many-Objective Optimization of a Hybrid Car Controller [chapter]

Tobias Rodemann, Kaname Narukawa, Michael Fischer, Mohammed Awada
2015 Lecture Notes in Computer Science  
In the multi-noisy-objective optimization problem of the SAW filter, the worst-case performance of a solution is considered based on the upper bounds of respective noisy-objective functions predicted statistically  ...  Multimemetic Algorithms in Dynamic Scale-Free Networks Rafael Nogueras, Carlos Cotta We study the behavior and performance of island-based multimemetic algorithms,  ...  The approach substitutes analytical sensitivity information by an update signal represented by a neural network approximation model.  ... 
doi:10.1007/978-3-319-16549-3_48 fatcat:yvlv6xnggnf3jn7vdnnwih6chy
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