Results 221 to 230 of about 86,374 (257)
Some of the next articles are maybe not open access.
Embedded Machine Learning for Machine Condition Monitoring
2021With the application of a new generation of information technology in the field of manufacturing and the deep integration of computer technology and manufacturing, industrial production is moving towards intellectualization and networking . Because the current production system cannot fully exploit the value of industrial data and the existence of ...
Michael Grethler +2 more
openaire +2 more sources
Rethinking Embedded Blocks for Machine Learning Applications
ACM Transactions on Reconfigurable Technology and Systems, 2021The underlying goal of FPGA architecture research is to devise flexible substrates that implement a wide variety of circuits efficiently. Contemporary FPGA architectures have been optimized to support networking, signal processing, and image processing applications through high-precision digital signal processing (DSP) blocks.
Seyedramin Rasoulinezhad +4 more
openaire +1 more source
Machine Learning: Metrics and Embeddings
2022In this thesis, we analyze new theories of clustering, one of the most fundamental tasks in machine learning. We use methods drawing from multiple disciplines, including metric embeddings, spectral algorithms, and group representation theory. 1.We propose a metric that adapts to the shape of data, and show how to quickly compute it.
openaire +1 more source
LightEQ: On-Device Earthquake Detection with Embedded Machine Learning
Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, 2023The detection of earthquakes in seismological time series is central to observational seismology. Generally, seismic sensors passively record data and transmit it to the cloud or edge for integration, storage, and processing. However, transmitting raw data through the network is not an option for sensors deployed in harsh environments like underwater ...
Tayyaba Zainab +2 more
openaire +1 more source
Extreme Learning Machine for Joint Embedding and Clustering
Neurocomputing, 2018Abstract Clustering generic data, i.e., data not specific to a particular field, is a challenging problem due to their diverse complex structures in the original feature space. Traditional approaches address this problem by complementing clustering with feature learning methods, which either capture the intrinsic structure of the data or represent ...
Tianchi Liu 0001 +3 more
openaire +2 more sources
Approximate Computing Methods for Embedded Machine Learning
2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS), 2018Embedding Machine Learning enables integrating intelligence in recent application domains such as Internet of Things, portable healthcare systems, and wearable devices. This paper presents an assessment of approximate computing methods at algorithmic, architecture, and circuit levels and draws perspectives for further developments and applications. The
Ali Ibrahim +5 more
openaire +1 more source
Towards Machine Learning Support for Embedded System Tests
2021 24th Euromicro Conference on Digital System Design (DSD), 2021The correctness of embedded systems needs to be ensured by a high number of tests. Large amounts of data reflecting the system behavior are collected during these test runs. Automated test evaluations are often limited to checking very specific requirements which can hardly cover all possible kinds of erroneous behaviors.
Stefan Scharoba +4 more
openaire +1 more source
Overview of the state of the art in embedded machine learning
2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2018Nowadays, the main challenges in embedded machine learning are related to artificial neural networks. Inspired by the biological neural networks, artificial neural networks are able to solve complex problems, by performing a tremendous amount of relatively simple parallel computations.
Liliana Andrade +2 more
openaire +1 more source
Machine Learning-Based Embedding for Discontinuous Time Series Machine Data
2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019This paper presents a machine learning-based dimension reduction framework (ML-framework). The ML-framework is designed to circumvent the challenges of high-dimensional discontinuous machine data applied in machine learning-based predictive maintenance analysis.
Oluseun Omotola Aremu +2 more
openaire +2 more sources
Network embedding based on deep extreme learning machine
International Journal of Machine Learning and Cybernetics, 2018Network embedding, which learns low-dimensional representations for each node with the goal of capturing and preserving the complex structure of original networks, has shown its necessity in network analysis. The structure of real-world networks is highly non-linear; however, most existing methods cannot be well applied due to their shallow models ...
Yunfei Chu +3 more
openaire +1 more source