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| 2. |
- Abdullah, Jakaria, et al.
(författare)
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Towards a Tool : TIMES-Pro for Modeling, Analysis, Simulation and Implementation of Cyber-Physical Systems
- 2017
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Ingår i: MODELS, ALGORITHMS, LOGICS AND TOOLS. - Cham : SPRINGER INTERNATIONAL PUBLISHING AG. - 9783319631219 - 9783319631202 ; , s. 623-639
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Konferensbidrag (refereegranskat)abstract
- We consider a Cyber-Physical System (CPS) as a network of components that are either physical plants with continuous behaviors or discrete controllers. To build CPS's in a systematic manner, the TIMES-Pro tool is designed to support modeling, analysis and code generation for real-time simulation and final deployment. In this paper, we present our decisions in designing the modeling language, the tool architecture and features of TIMES-Pro, and also a case study to demonstrate its applicability.
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| 3. |
- Abdullah, Jakaria, et al.
(författare)
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Worst-Case Cause-Effect Reaction Latency in Systems with Non-Blocking Communication
- 2019
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Ingår i: Design, Automation & Test in Europe Conference & Exhibition. - : IEEE. - 9783981926323 ; , s. 1625-1630
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Konferensbidrag (refereegranskat)abstract
- In real-time embedded systems, a system functionality is often implemented using a data-flow chain over a set of communicating tasks. A critical non-functional requirement in such systems is to restrict the amount of time, i.e. cause-effect latency, for an input to impact its corresponding output. The problem of estimating the worst-case cause-effect latency is well-studied in the context of blocking inter-task communication. Recent research results show that non-blocking communication preserving functional semantics is critical for the model-based design of dynamically updatable systems. In this paper, we study the worst-case cause-effect reaction latency estimation problem in the context of non-blocking inter-task communication. We present a computationally efficient algorithm that tightly over-approximates the exact worst-case reaction latency in cause-effect data-flow chains.
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| 4. |
- Dai, Gaoyang, et al.
(författare)
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Response-Time Analysis of Limited-Preemptive Sporadic DAG Tasks
- 2022
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Ingår i: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0070 .- 1937-4151. ; 41:11, s. 3673-3684
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Tidskriftsartikel (refereegranskat)abstract
- Guaranteeing timing constraints for parallel real-time applications deployed on multicore platforms is challenging, especially for applications containing non-preemptive execution blocks, that suffer from priority inversions. In this article, we propose to model such applications using a sporadic directed acyclic graph (DAG) model where preemption may take place only between the nodes of a DAG task. We present a new method for response-time analysis of such tasks scheduled with the global fixed-priority scheduling policy. We show that our method outperforms the state-of-the-art techniques significantly in terms of resource utilization in experimental evaluations using both benchmark and randomly generated task sets. We also present a method to deal with global EDF scheduling, which is a new technique proposed for response time analysis of sporadic DAG tasks with non-preemptive nodes.
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| 5. |
- Dai, Gaoyang, et al.
(författare)
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Timing-Anomaly Free Dynamic Scheduling of Periodic DAG Tasks with Non-Preemptive Nodes
- 2021
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Ingår i: 2021 IEEE 27th International Conference On Embedded And Real-Time Computing Systems And Applications (RTCSA 2021). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665441889 ; , s. 119-128
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Konferensbidrag (refereegranskat)abstract
- Designing timing-anomaly free multiprocessor scheduling algorithms is a notoriously hard problem, especially for parallel tasks with non-preemptive execution regions. In this paper, we first propose a simple yet expressive model which abstracts a parallel task as a single computation unit, and then, present a sufficient condition for timing-anomaly free scheduling of such units. On top of this, we design an algorithm for scheduling a set of periodic parallel tasks, represented as DAG with non-preemptive subtasks, on multicore processors. The algorithm has several desirable properties, including timing-anomaly freedom, high resource utilization, and low memory requirement. Timing-anomaly freedom enables an exact schedulability test for the algorithm, which, as shown in our evaluations, provides a significantly high schedulability ratio compared to those state-of-the-art methods that suffer from timing anomalies.
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| 7. |
- Mohaqeqi, Morteza, et al.
(författare)
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Counting Priority Inversions : Computing Maximum Additional Core Requests of DAG Tasks
- 2022
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Ingår i: Proceedings Of The 2022 Design, Automation & Test In Europe Conference & Exhibition (DATE 2022). - : Institute of Electrical and Electronics Engineers (IEEE). - 9783981926361 ; , s. 1281-1286
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Konferensbidrag (refereegranskat)abstract
- Many parallel real-time applications can be modeled as DAG tasks. Guaranteeing timing constraints of such applications executed on multicore systems is challenging, especially for the applications with non-preemptive execution blocks. The existing approach for timing analysis of such tasks with sporadic release relies on computing a bound on the interfering workload on a task, which depends on the number of priority inversions the task may experience. The number of priority inversions, in turn, is a function of the total number of additional cores a task instance may request after each node spawning. In this paper, we show that the previously proposed polynomial-time algorithm to compute the maximum number of additional core requests of a DAG is not correct, providing a counter example. We show that the problem is in fact NP-hard. We then present an ILP formulation as an exact solution to the problem. Our evaluations show that the problem can be solved in a few minutes even for DAGs with hundreds of nodes.
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| 8. |
- Yu, Wenjin, et al.
(författare)
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Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid
- 2022
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 12, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.
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