ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    facet.materialart.
    Unknown
    Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-04-04
    Description: Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today’s world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 2
    facet.materialart.
    Unknown
    An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-07-04
    Description: The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother. However, cumbersome algorithms that require the reference maternal ECG as well as heavy feature crafting makes out-of-clinics fECG monitoring in daily life not yet feasible. To address these challenges, we proposed a pure end-to-end deep learning model to detect fetal QRS complexes (i.e., the main spikes observed on a fetal ECG waveform). Additionally, the model has the residual network (ResNet) architecture that adopts the novel 1-D octave convolution (OctConv) for learning multiple temporal frequency features, which in turn reduce memory and computational cost. Importantly, the model is capable of highlighting the contribution of regions that are more prominent for the detection. To evaluate our approach, data from the PhysioNet 2013 Challenge with labeled QRS complex annotations were used in the original form, and the data were then modified with Gaussian and motion noise, mimicking real-world scenarios. The model can achieve a F1 score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 3
    facet.materialart.
    Unknown
    Neural correlates of sparse coding and dimensionality reduction (2019)
    Public Library of Science
    Details
    Publication Date: 2019-06-27
    Print ISSN: 1553-734X
    Electronic ISSN: 1553-7358
    Topics: Biology , Computer Science
    Published by Public Library of Science
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 4
    facet.materialart.
    Unknown
    Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-03-24
    Description: Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy is needed to identify possible complications early and ensure the mother’s and her unborn baby’s health and well-being. Different studies have thus far proposed maternal health monitoring systems. However, they are designed for a specific health problem or are limited to questionnaires and short-term data collection methods. Moreover, the requirements and challenges have not been evaluated in long-term studies. Maternal health necessitates a comprehensive framework enabling continuous monitoring of pregnant women. In this paper, we present an Internet-of-Things (IoT)-based system to provide ubiquitous maternal health monitoring during pregnancy and postpartum. The system consists of various data collectors to track the mother’s condition, including stress, sleep, and physical activity. We carried out the full system implementation and conducted a real human subject study on pregnant women in Southwestern Finland. We then evaluated the system’s feasibility, energy efficiency, and data reliability. Our results show that the implemented system is feasible in terms of system usage during nine months. We also indicate the smartwatch, used in our study, has acceptable energy efficiency in long-term monitoring and is able to collect reliable photoplethysmography data. Finally, we discuss the integration of the presented system with the current healthcare system.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 5
    facet.materialart.
    Unknown
    RT Component Sets for High-Level Design Applications (1997)
    Hindawi
    Details
    Publication Date: 1997-01-01
    Description: The system-level design process typically involves refining a design specification down to the point where each of the system's components is described as a block diagram or netlist of abstract Register-Transfer (RT) level components. In this paper, we motivate the need for such a standard RT component set, and describe a library environment that supports automatic model generation, design reuse, and synthesis with technology-specific estimators. We demonstrate the efficacy of the standard RT-component set approach with experiments performed on the HLSW92 benchmarks. Our preliminary results indicate only a small overhead of about 10% in using these standard, generic components. We then describe an automatic model generation and technology projection scheme that uses fast (on-line) estimators for predicting the area and delay of generic RT components tuned to a particular technology library with an accuracy of 10%. These model generators and estimators have been integrated with a high-level synthesis system at U.C. Irvine.
    Print ISSN: 1065-514X
    Electronic ISSN: 1563-5171
    Topics: Electrical Engineering, Measurement and Control Technology
    Published by Hindawi
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 6
    facet.materialart.
    Unknown
    Resilient dependable cyber-physical systems: a middleware perspective (2012)
    Springer
    Details
    Publication Date: 2012-01-28
    Description: In this paper, we address the role of middleware in enabling robust and resilient cyber-physical systems (CPSs) of the future. In particular, we will focus on how adaptation services can be used to improve dependability in instrumented cyber-physical systems based on the principles of “computational reflection.” CPS environments incorporate a variety of sensing and actuation devices in a distributed architecture; such a deployment is used to create a digital representation of the evolving physical world and its processes for use by a broad range of applications. CPS applications, in particular, mission critical tasks, must execute dependably despite disruptions caused by failures and limitations in sensing, communications, and computation. This paper discusses a range of applications, their reliability needs, and potential dependability holes that can cause performance degradation and application failures. In particular, we distinguish between the notion of infrastructure and information dependability and illustrate the need to formally model and reason about a range of CPS applications and their dependability needs. Formal methods based tools can help us design meaningful cross-layer adaptation techniques at different system layers of the CPS environment and thereby achieve end-to-end dependability at both the infrastructure and information levels.
    Print ISSN: 1867-4828
    Electronic ISSN: 1869-0238
    Topics: Computer Science
    Published by Springer
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 7
    facet.materialart.
    Unknown
    Dependable Embedded Systems (2021)
    Springer Nature | Springer International Publishing
    Details
    Publication Date: 2024-04-11
    Description: This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems.
    Keywords: Circuits and Systems ; Cyber-physical systems, IoT ; Processor Architectures ; Electronic Circuits and Systems ; Cyber-Physical Systems ; Fault-Tolerant Computing ; Reliability Enhancers in Embedded Systems ; SoCs based on cross-layer-reliability ; System-Level Reliability Analysis ; Dependable Software Execution ; Open access ; Electronics: circuits & components ; Electrical engineering ; Cybernetics & systems theory ; Computer architecture & logic design ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFC Electronics: circuits and components ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYF Computer architecture and logic design
    Language: English
    Format: image/jpeg
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    E-BOOK
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...