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An efficient time representation for real-time embedded systems

Alessio Carlini, Giorgio C. Buttazzo
2003 Proceedings of the 2003 ACM symposium on Applied computing - SAC '03  
doi:10.1145/952660.952669 fatcat:dmo23m7itrd3bhmi256dh6zx2q

An efficient time representation for real-time embedded systems

Alessio Carlini, Giorgio C. Buttazzo
2003 Proceedings of the 2003 ACM symposium on Applied computing - SAC '03  
doi:10.1145/952532.952669 dblp:conf/sac/CarliniB03 fatcat:loa5vvykjnf43oyiftug3l6sky

A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations [article]

Aditya Mantha, Yokila Arora, Shubham Gupta, Praveenkumar Kanumala, Zhiwei Liu, Stephen Guo, Kannan Achan
2020 arXiv   pre-print
In this paper, we introduce a production within-basket grocery recommendation system, RTT2Vec, which generates real-time personalized product recommendations to supplement the user's current grocery basket  ...  We also propose an approximate inference technique 11.6x times faster than exact inference approaches.  ...  These embeddings are then stored in an embedding store (distributed cache) to facilitate online retrieval by the real-time inference engine.  ... 
arXiv:1910.12757v3 fatcat:azhtn2kd7vda3b2t7skgctmoie

GUEST EDITORIAL special issue on real-time perceptual-inspired imaging systems with computational science and aesthetics

Sanghyun Seo, Bo-Wei Chen, Periklis Chatzimisios, Seungmin Rho
2017 Journal of Real-Time Image Processing  
His research interests are in the area of computer graphics, nonphotorealistic rendering and animation, 3D GIS system, real-time rendering using GPU, AR (augmented reality), and game technology.  ...  Journal of Real-Time Image Processing, Future Generation Computer Systems, Engineering Applications of Artificial Intelligence, New Review of Hypermedia and Multimedia, Multimedia Tools and Applications  ...  [1] proposed a graph-based image representation model with an efficient matching algorithm for real-time image retrieval.  ... 
doi:10.1007/s11554-017-0716-1 fatcat:g3huxkznnzcpbgywepg7vxtbru

AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection [article]

Yanfang Ye, Shifu Hou, Lingwei Chen, Jingwei Lei, Wenqiang Wan, Jiabin Wang, Qi Xiong, Fudong Shao
2019 arXiv   pre-print
Promising experimental results demonstrate that our developed system AiDroid which integrates our proposed method outperforms others in real-time Android malware detection.  ...  To efficiently classify nodes (e.g., apps) in the constructed HIN, we propose the HinLearning method to first obtain in-sample node embeddings and then learn representations of out-of-sample nodes without  ...  Acknowledgement The authors would also like to thank the anti-malware experts of Tencent Security Lab (Yinming Mei, Yuanhai Luo, Hong Yi, and Kui Wang) for helpful discussion and implementation. Y.  ... 
arXiv:1811.01027v2 fatcat:v7bea5oswbe6vpzxs23ab4k2ka

Guest Editorial for Special Issue of ESWEEK 2015

Petru Eles, Rolf Ernst
2016 ACM Transactions on Embedded Computing Systems  
Embedded Systems Week (ESWEEK) is the premier event covering all aspects of embedded systems and software.  ...  Manfred Morari, professor at ETH Zuerich, and one of the most prominent researchers in automated control, demonstrated the importance of embedded system performance for cyber-physical systems in the keynote  ...  The authors of "An Efficient Technique of Application Mapping and Scheduling on Real-Time Multiprocessor Systems for Throughput Optimization" consider a homogeneous multiprocessor architecture.  ... 
doi:10.1145/2968218 fatcat:was6e46scvcbfmlbdirffmdwdu

Special issue on real-time image and video processing in mobile embedded systems

Awais Ahmad, Marco Anisetti, Ernesto Damiani, Gwanggil Jeon
2018 Journal of Real-Time Image Processing  
This special issue is intended to provide a highly recognized international forum to present recent advances in Real-Time Image and Video Processing in Mobile Embedded Systems.  ...  results, projects, surveying works and industrial experiences that are dealing with theory and applications within the theme of Real-Time Image and Video Processing in Mobile Embedded Systems.  ...  Acknowledgements We would like to express our appreciation to all the authors for their informative contributions and the reviewers for their support and constructive critiques in making this special issue  ... 
doi:10.1007/s11554-018-0842-4 fatcat:tefzgwthhjfcvfrfch3xtmfcpm

Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning

Dimitrios Boursinos, Xenofon Koutsoukos
2021 Artificial intelligence for engineering design, analysis and manufacturing  
In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations.  ...  Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.  ...  We focus on computationally efficient algorithms that can be used for real-time monitoring.  ... 
doi:10.1017/s089006042100010x fatcat:w3amwshbing4hoparcg3cqc2sy

An Implementation of Real Time-Sentential KSSL Recognition System Based on the Post Wearable PC [chapter]

Jung-Hyun Kim, Yong-Wan Roh, Kwang-Seok Hong
2006 Lecture Notes in Computer Science  
The experimental result shows an average recognition rate of 93.7% for continuous 44 KSSL sentences.  ...  recognizes and represents continuous KSSL with flexibility in real time, and analyze and notify definite intention of user more efficiently through correct measurement of KSSL gestures using wireless haptic  ...  for an alternative design methodology which can be applied in developing both linear and nonlinear systems for embedded control and has been found to be very suitable for embedded control applications  ... 
doi:10.1007/11758549_118 fatcat:ztny4ebmyzendaj7jwb6rivzqe

Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learning [article]

Dimitrios Boursinos, Xenofon Koutsoukos
2021 arXiv   pre-print
The proposed method is computationally efficient, and therefore, can be used in real-time.  ...  In this paper, we use the Inductive Venn Predictors framework for computing probability intervals regarding the correctness of each prediction in real-time.  ...  Our choice of lightweight DNNs and small embedding representation size make the approach computationally efficient and can be used in real-time.  ... 
arXiv:2110.03127v1 fatcat:4xfny7kbovduzddfqm4ocatfjm

Laser2Vec: Similarity-based Retrieval for Robotic Perception Data [article]

Samer B. Nashed
2020 arXiv   pre-print
The accuracy, robustness, scalability, and efficiency of our system is tested on real-world data gathered from dozens of deployments and synthetic data generated by corrupting real data.  ...  for complete or partial scans efficiently.  ...  We determine empirically, based on the local continuity of the embedded space. To estimate , we let scan s i have an embedded representation v i and let be any vector with magnitude .  ... 
arXiv:2007.15746v1 fatcat:trzr4k46xvbhfmo6yljenalfza

Efficient and Effective Multi-Modal Queries through Heterogeneous Network Embedding

Chi Thang Duong, Tam Thanh Nguyen, Hongzhi Yin, Matthias Weidlich, Son Mai, Karl Aberer, Quoc Viet Hung Nguyen
2021 IEEE Transactions on Knowledge and Data Engineering  
Our experimental results for real-world and synthetic datasets illustrate the efficiency and effectiveness of our approach.  ...  CONCLUSIONS In this paper, we presented a new direction for multimodal IR that relies on an embedding of a heterogeneous information network.  ...  We observe that the training time is extremely short, around 40min, for an embedding size of 32. This shows that our method is able to handle large networks.  ... 
doi:10.1109/tkde.2021.3052871 fatcat:eff3dykxhbhgzeywvpfkjswcju

Modified RLE with Timestamp Storage for Slow Gradient Variable Data

Chandan Maity, Ashutosh Gupta, Sanjat Kumar Panigrahi
2013 International Journal of Computer Applications  
The ultra-low power embedded devices are generally batteryoperated and have limited data memory. The efficient use of memory is very important as the data memory of the device is limited.  ...  to extend the life as long as possible by maximizing the time of sleep mode.  ...  Thus, the memory allocated for storing temperature data can be compressed in real time, thereby increasing the efficiency of the system.  ... 
doi:10.5120/10080-4694 fatcat:qgegkh36vbe33di3jm3metxoxa

Out-of-sample Node Representation Learning for Heterogeneous Graph in Real-time Android Malware Detection

Yanfang Ye, Shifu Hou, Lingwei Chen, Jingwei Lei, Wenqiang Wan, Jiabin Wang, Qi Xiong, Fudong Shao
2019 Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence  
We later design a deep neural network classifier taking the learned HG representations as inputs for real-time Android malware detection.  ...  Promising results demonstrate that our developed system AiDroid which integrates our proposed method outperforms others in real-time Android malware detection.  ...  We develop a system AiDroid (shown in Figure 2 ) integrating our proposed method for real-time Android malware detection, which has major merits of: • Besides runtime behaviors, we further analyze the  ... 
doi:10.24963/ijcai.2019/576 dblp:conf/ijcai/YeHCLWWXS19 fatcat:l3ipnoyswbeqnogqfdcpjrssme

On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning [article]

Bharat Prakash, Mark Horton, Nicholas R. Waytowich, William David Hairston, Tim Oates, Tinoosh Mohsenin
2019 arXiv   pre-print
This compression model is vital to efficiently learn policies, especially when learning on embedded systems.  ...  In autonomous embedded systems, it is often vital to reduce the amount of actions taken in the real world and energy required to learn a policy.  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.  ... 
arXiv:1903.10404v1 fatcat:4uh3oo42cbdibjph5pzhkct7xy
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