scholarly journals WCET-Aware Dynamic I-Cache Locking for a Single Task

2017 ◽  
Vol 14 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Wenguang Zheng ◽  
Hui Wu ◽  
Qing Yang
Keyword(s):  
Single Task ◽  
Cache Locking

scholarly journals A Dynamic Instruction Cache Locking Approach for Minimizing Worst Case Execution Time of a Single Task

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 208003-208015
Author(s):  
Tingxu Zhang ◽  
Wenguang Zheng ◽  
Yingyuan Xiao ◽  
Guangping Xu
Keyword(s):  
Execution Time ◽  
Worst Case ◽  
Single Task ◽  
Instruction Cache ◽  
Worst Case Execution Time ◽  
Cache Locking

WCET-Aware Dynamic D-cache Locking for A Single Task

2015 ◽  
Vol 50 (5) ◽  
pp. 1-10
Author(s):  
Wenguang Zheng ◽  
Hui Wu
Keyword(s):  
Single Task ◽  
Cache Locking

Effects of Concurrent Manual Task Performance on Connected Speech Acoustics in Individuals With Parkinson Disease

2019 ◽  
Vol 62 (7) ◽  
pp. 2099-2117 ◽  
Author(s):  
Jason A. Whitfield ◽  
Zoe Kriegel ◽  
Adam M. Fullenkamp ◽  
Daryush D. Mehta
Keyword(s):  
Task Performance ◽  
Speech Production ◽  
Dual Task ◽  
Motor Task ◽  
Task Condition ◽  
Connected Speech ◽  
Speech Acoustics ◽  
Single Task ◽  
Manual Task ◽  
Task Conditions

Purpose Prior investigations suggest that simultaneous performance of more than 1 motor-oriented task may exacerbate speech motor deficits in individuals with Parkinson disease (PD). The purpose of the current investigation was to examine the extent to which performing a low-demand manual task affected the connected speech in individuals with and without PD. Method Individuals with PD and neurologically healthy controls performed speech tasks (reading and extemporaneous speech tasks) and an oscillatory manual task (a counterclockwise circle-drawing task) in isolation (single-task condition) and concurrently (dual-task condition). Results Relative to speech task performance, no changes in speech acoustics were observed for either group when the low-demand motor task was performed with the concurrent reading tasks. Speakers with PD exhibited a significant decrease in pause duration between the single-task (speech only) and dual-task conditions for the extemporaneous speech task, whereas control participants did not exhibit changes in any speech production variable between the single- and dual-task conditions. Conclusions Overall, there were little to no changes in speech production when a low-demand oscillatory motor task was performed with concurrent reading. For the extemporaneous task, however, individuals with PD exhibited significant changes when the speech and manual tasks were performed concurrently, a pattern that was not observed for control speakers. Supplemental Material https://doi.org/10.23641/asha.8637008

THE RELIABILITY OF CLINICAL BALANCE TESTS UNDER SINGLE-TASK AND DUAL-TASK TESTING PARADIGMS IN UNINJURED ACTIVE YOUTH AND YOUNG ADULTS

2020 ◽  
Vol 15 (4) ◽  
pp. 487-500
Author(s):  
Thaer S. Manaseer ◽  
Jackie L. Whittaker ◽  
Codi Isaac ◽  
Kathryn Schneider ◽  
Mary Roduta Roberts ◽  
...  
Keyword(s):  
Young Adults ◽  
Dual Task ◽  
Single Task ◽  
Balance Tests ◽  
Youth And Young Adults

ANALISIS DAN PERANCANGAN MATERIAL HANDLING DENGAN PERHITUNGAN NIOSH LIFTING EQUATION SINGLE TASK

2018 ◽  
Vol 23 (1) ◽  
pp. 56-65
Author(s):  
Moehamad Adi Rochmat
Keyword(s):  
Material Handling ◽  
Occupational Safety ◽  
Occupational Safety And Health ◽  
Single Task ◽  
Safety And Health ◽  
Microsoft Office ◽  
Niosh Lifting Equation

Revised NIOSH Lifting Equation (RNLE) merupakan sebuah aplikasi yang dibuat oleh National Institute for Occupational Safety and Health (NIOSH) yang merupakan sebuah institusi di Amerika yang mengembangkan perangkat penilaian dalam bidang keselamatan dan kesehatan kerja. Salah satu aplikasi yang dikembangkan dinamakan Revised NIOSH Lifting Equation Single Task yang digunakan untuk menguji aktifitas pemindahan barang tanpa perpindahan posisi kaki. Aplikasi yang dibangun akan memberikan penilaian terhadap sistem kerja yang dilakukan oleh seorang pekerja. Salah satu hasil perhitungan dari aplikasi tersebut adalah nilai Lifting Index (LI) yang menyatakan tingkat resiko pekerjaan. Aplikasi RNLE dikembangkan dengan dasar program Microsoft Office Excel. Rumus perhitungan untuk mendapatkan nilai LI dan parameter yang diperlukan disediakan oleh NIOSH dan dapat dipelajari. Penelitian ini mengembangkan rekomendasi perbaikan sistem kerja pada aplikasi RNLE dengan menggunakan program Microsoft Office Excel. Rekomendasi perbaikan yang diutamakan adalah posisi awal benda dan posisi akhir yang sebaiknya diatur sedemikian sehingga mengoptimalkan kemampuan pekerja. Cara optimalisasi yang bisa dilakukan dengan merubah salah satu nilai parameter masukkan tanpa merubah nilai yang lainnya.

Digital Biomarkers of Cognitive Frailty: The Value of Detailed Gait Assessment Beyond Gait Speed

Gerontology ◽  
2021 ◽  
pp. 1-10
Author(s):  
He Zhou ◽  
Catherine Park ◽  
Mohammad Shahbazi ◽  
Michele K. York ◽  
Mark E. Kunik ◽  
...  
Keyword(s):  
Older Adults ◽  
Steady State ◽  
Dual Task ◽  
Gait Speed ◽  
Stride Length ◽  
Cognitive Frailty ◽  
Single Task ◽  
Double Support ◽  
Gait Parameters ◽  
Digital Biomarkers

<b><i>Background:</i></b> Cognitive frailty (CF), defined as the simultaneous presence of cognitive impairment and physical frailty, is a clinical symptom in early-stage dementia with promise in assessing the risk of dementia. The purpose of this study was to use wearables to determine the most sensitive digital gait biomarkers to identify CF. <b><i>Methods:</i></b> Of 121 older adults (age = 78.9 ± 8.2 years, body mass index = 26.6 ± 5.5 kg/m<sup>2</sup>) who were evaluated with a comprehensive neurological exam and the Fried frailty criteria, 41 participants (34%) were identified with CF and 80 participants (66%) were identified without CF. Gait performance of participants was assessed under single task (walking without cognitive distraction) and dual task (walking while counting backward from a random number) using a validated wearable platform. Participants walked at habitual speed over a distance of 10 m. A validated algorithm was used to determine steady-state walking. Gait parameters of interest include steady-state gait speed, stride length, gait cycle time, double support, and gait unsteadiness. In addition, speed and stride length were normalized by height. <b><i>Results:</i></b> Our results suggest that compared to the group without CF, the CF group had deteriorated gait performances in both single-task and dual-task walking (Cohen’s effect size <i>d</i> = 0.42–0.97, <i>p</i> &#x3c; 0.050). The largest effect size was observed in normalized dual-task gait speed (<i>d</i> = 0.97, <i>p</i> &#x3c; 0.001). The use of dual-task gait speed improved the area under the curve (AUC) to distinguish CF cases to 0.76 from 0.73 observed for the single-task gait speed. Adding both single-task and dual-task gait speeds did not noticeably change AUC. However, when additional gait parameters such as gait unsteadiness, stride length, and double support were included in the model, AUC was improved to 0.87. <b><i>Conclusions:</i></b> This study suggests that gait performances measured by wearable sensors are potential digital biomarkers of CF among older adults. Dual-task gait and other detailed gait metrics provide value for identifying CF above gait speed alone. Future studies need to examine the potential benefits of gait performances for early diagnosis of CF and/or tracking its severity over time.

scholarly journals A Multi-Branch U-Net for Steel Surface Defect Type and Severity Segmentation

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 870
Author(s):  
Robby Neven ◽  
Toon Goedemé
Keyword(s):  
Process Parameters ◽  
Visual Inspection ◽  
Steel Surface ◽  
Surface Defect ◽  
State Of The Art ◽  
Sheet Steel ◽  
Real Life ◽  
Single Task ◽  
Severity Estimation ◽  
Task Model

Automating sheet steel visual inspection can improve quality and reduce costs during its production. While many manufacturers still rely on manual or traditional inspection methods, deep learning-based approaches have proven their efficiency. In this paper, we go beyond the state-of-the-art in this domain by proposing a multi-task model that performs both pixel-based defect segmentation and severity estimation of the defects in one two-branch network. Additionally, we show how incorporation of the production process parameters improves the model’s performance. After manually constructing a real-life industrial dataset, we first implemented and trained two single-task models performing the defect segmentation and severity estimation tasks separately. Next, we compared this to a multi-task model that simultaneously performs the two tasks at hand. By combining the tasks into one model, both segmentation tasks improved by 2.5% and 3% mIoU, respectively. In the next step, we extended the multi-task model using sensor fusion with process parameters. We demonstrate that the incorporation of the process parameters resulted in a further mIoU increase of 6.8% and 2.9% for the defect segmentation and severity estimation tasks, respectively.

Identification of Mild Cognitive Impairment Among Chinese Based on Multiple Spoken Tasks

2021 ◽  
pp. 1-20
Author(s):  
Tianqi Wang ◽  
Yin Hong ◽  
Quanyi Wang ◽  
Rongfeng Su ◽  
Manwa Lawrence Ng ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Memory Load ◽  
Lexical Retrieval ◽  
Semantic Fluency ◽  
Late Fusion ◽  
Single Task ◽  
Sentence Repetition ◽  
Picture Description ◽  
Description Task

Background: Previous studies explored the use of noninvasive biomarkers of speech and language for the detection of mild cognitive impairment (MCI). Yet, most of them employed single task which might not have adequately captured all aspects of their cognitive functions. Objective: The present study aimed to achieve the state-of-the-art accuracy in detecting individuals with MCI using multiple spoken tasks and uncover task-specific contributions with a tentative interpretation of features. Methods: Fifty patients clinically diagnosed with MCI and 60 healthy controls completed three spoken tasks (picture description, semantic fluency, and sentence repetition), from which multidimensional features were extracted to train machine learning classifiers. With a late-fusion configuration, predictions from multiple tasks were combined and correlated with the participants’ cognitive ability assessed using the Montreal Cognitive Assessment (MoCA). Statistical analyses on pre-defined features were carried out to explore their association with the diagnosis. Results: The late-fusion configuration could effectively boost the final classification result (SVM: F1 = 0.95; RF: F1 = 0.96; LR: F1 = 0.93), outperforming each individual task classifier. Besides, the probability estimates of MCI were strongly correlated with the MoCA scores (SVM: –0.74; RF: –0.71; LR: –0.72). Conclusion: Each single task tapped more dominantly to distinct cognitive processes and have specific contributions to the prediction of MCI. Specifically, picture description task characterized communications at the discourse level, while semantic fluency task was more specific to the controlled lexical retrieval processes. With greater demands on working memory load, sentence repetition task uncovered memory deficits through modified speech patterns in the reproduced sentences.

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