@article{MichallekGenskeNiehuesetal.2022, author = {Michallek, Florian and Genske, Ulrich and Niehues, Stefan Markus and Hamm, Bernd and Jahnke, Paul}, title = {Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging}, series = {European Radiology}, volume = {32}, journal = {European Radiology}, number = {7}, publisher = {Springer}, address = {New York}, issn = {1432-1084}, doi = {10.1007/s00330-022-08592-y}, pages = {4587 -- 4595}, year = {2022}, abstract = {Objectives To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST). Methods Effects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient's abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient >= 0.75), discriminative power (Kruskal-Wallis test p value < 0.05), and repeatability (overall concordance correlation coefficient >= 0.75). Results The median fraction of consistent features across all doses was 6\%, 8\%, 6\%, and 22\% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48\%, 82\%, 84\%, and 92\% of features, and 52\%, 20\%, 17\%, and 39\% of features were repeatable, respectively. Only 5\% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13\% with AiCE at doses above 1 mGy and 17\% at doses >= 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features. Conclusions AiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features.}, language = {en} } @article{BandyopadhyaySarkarMandaletal.2022, author = {Bandyopadhyay, Soumyadip and Sarkar, Dipankar and Mandal, Chittaranjan and Giese, Holger}, title = {Translation validation of coloured Petri net models of programs on integers}, series = {Acta informatica}, volume = {59}, journal = {Acta informatica}, number = {6}, publisher = {Springer}, address = {New York}, issn = {0001-5903}, doi = {10.1007/s00236-022-00419-z}, pages = {725 -- 759}, year = {2022}, abstract = {Programs are often subjected to significant optimizing and parallelizing transformations based on extensive dependence analysis. Formal validation of such transformations needs modelling paradigms which can capture both control and data dependences in the program vividly. Being value-based with an inherent scope of capturing parallelism, the untimed coloured Petri net (CPN) models, reported in the literature, fit the bill well; accordingly, they are likely to be more convenient as the intermediate representations (IRs) of both the source and the transformed codes for translation validation than strictly sequential variable-based IRs like sequential control flow graphs (CFGs). In this work, an efficient path-based equivalence checking method for CPN models of programs on integers is presented. Extensive experimentation has been carried out on several sequential and parallel examples. Complexity and correctness issues have been treated rigorously for the method.}, language = {en} } @article{ChenLangeAndjelkovicetal.2022, author = {Chen, Junchao and Lange, Thomas and Andjelkovic, Marko and Simevski, Aleksandar and Lu, Li and Krstić, Miloš}, title = {Solar particle event and single event upset prediction from SRAM-based monitor and supervised machine learning}, series = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers}, volume = {10}, journal = {IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers}, number = {2}, publisher = {Institute of Electrical and Electronics Engineers}, address = {[New York, NY]}, issn = {2168-6750}, doi = {10.1109/TETC.2022.3147376}, pages = {564 -- 580}, year = {2022}, abstract = {The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.}, language = {en} } @article{BreitenreiterAndjelkovićSchrapeetal.2022, author = {Breitenreiter, Anselm and Andjelković, Marko and Schrape, Oliver and Krstić, Miloš}, title = {Fast error propagation probability estimates by answer set programming and approximate model counting}, series = {IEEE Access}, volume = {10}, journal = {IEEE Access}, publisher = {Inst. of Electr. and Electronics Engineers}, address = {Piscataway}, issn = {2169-3536}, doi = {10.1109/ACCESS.2022.3174564}, pages = {51814 -- 51825}, year = {2022}, abstract = {We present a method employing Answer Set Programming in combination with Approximate Model Counting for fast and accurate calculation of error propagation probabilities in digital circuits. By an efficient problem encoding, we achieve an input data format similar to a Verilog netlist so that extensive preprocessing is avoided. By a tight interconnection of our application with the underlying solver, we avoid iterating over fault sites and reduce calls to the solver. Several circuits were analyzed with varying numbers of considered cycles and different degrees of approximation. Our experiments show, that the runtime can be reduced by approximation by a factor of 91, whereas the error compared to the exact result is below 1\%.}, language = {en} } @article{AndjelkovicSimevskiChenetal.2022, author = {Andjelkovic, Marko and Simevski, Aleksandar and Chen, Junchao and Schrape, Oliver and Stamenkovic, Zoran and Krstić, Miloš and Ilic, Stefan and Ristic, Goran and Jaksic, Aleksandar and Vasovic, Nikola and Duane, Russell and Palma, Alberto J. and Lallena, Antonio M. and Carvajal, Miguel A.}, title = {A design concept for radiation hardened RADFET readout system for space applications}, series = {Microprocessors and microsystems}, volume = {90}, journal = {Microprocessors and microsystems}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0141-9331}, doi = {10.1016/j.micpro.2022.104486}, pages = {18}, year = {2022}, abstract = {Instruments for measuring the absorbed dose and dose rate under radiation exposure, known as radiation dosimeters, are indispensable in space missions. They are composed of radiation sensors that generate current or voltage response when exposed to ionizing radiation, and processing electronics for computing the absorbed dose and dose rate. Among a wide range of existing radiation sensors, the Radiation Sensitive Field Effect Transistors (RADFETs) have unique advantages for absorbed dose measurement, and a proven record of successful exploitation in space missions. It has been shown that the RADFETs may be also used for the dose rate monitoring. In that regard, we propose a unique design concept that supports the simultaneous operation of a single RADFET as absorbed dose and dose rate monitor. This enables to reduce the cost of implementation, since the need for other types of radiation sensors can be minimized or eliminated. For processing the RADFET's response we propose a readout system composed of analog signal conditioner (ASC) and a self-adaptive multiprocessing system-on-chip (MPSoC). The soft error rate of MPSoC is monitored in real time with embedded sensors, allowing the autonomous switching between three operating modes (high-performance, de-stress and fault-tolerant), according to the application requirements and radiation conditions.}, language = {en} } @article{RisticIlicAndjelkovicetal.2022, author = {Ristic, Goran S. and Ilic, Stefan D. and Andjelkovic, Marko S. and Duane, Russell and Palma, Alberto J. and Lalena, Antonio M. and Krstić, Miloš and Jaksic, Aleksandar B.}, title = {Sensitivity and fading of irradiated RADFETs with different gate voltages}, series = {Nuclear Instruments and Methods in Physics Research Section A}, volume = {1029}, journal = {Nuclear Instruments and Methods in Physics Research Section A}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0168-9002}, doi = {10.1016/j.nima.2022.166473}, pages = {7}, year = {2022}, abstract = {The radiation-sensitive field-effect transistors (RADFETs) with an oxide thickness of 400 nm are irradiated with gate voltages of 2, 4 and 6 V, and without gate voltage. A detailed analysis of the mechanisms responsible for the creation of traps during irradiation is performed. The creation of the traps in the oxide, near and at the silicon/silicon-dioxide (Si/SiO2) interface during irradiation is modelled very well. This modelling can also be used for other MOS transistors containing SiO2. The behaviour of radiation traps during postirradiation annealing is analysed, and the corresponding functions for their modelling are obtained. The switching traps (STs) do not have significant influence on threshold voltage shift, and two radiation-induced trap types fit the fixed traps (FTs) very well. The fading does not depend on the positive gate voltage applied during irradiation, but it is twice lower in case there is no gate voltage. A new dosimetric parameter, called the Golden Ratio (GR), is proposed, which represents the ratio between the threshold voltage shift after irradiation and fading after spontaneous annealing. This parameter can be useful for comparing MOS dosimeters.}, language = {en} }