TY - JOUR A1 - Ristic, Goran S. A1 - Ilic, Stefan D. A1 - Andjelkovic, Marko S. A1 - Duane, Russell A1 - Palma, Alberto J. A1 - Lalena, Antonio M. A1 - Krstić, Miloš A1 - Jaksic, Aleksandar B. T1 - Sensitivity and fading of irradiated RADFETs with different gate voltages JF - Nuclear Instruments and Methods in Physics Research Section A N2 - 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. KW - pMOS radiation dosimeter KW - RADFETs KW - irradiation KW - sensitivity KW - annealing KW - fading Y1 - 2022 U6 - https://doi.org/10.1016/j.nima.2022.166473 SN - 0168-9002 SN - 1872-9576 VL - 1029 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Andjelkovic, Marko A1 - Simevski, Aleksandar A1 - Chen, Junchao A1 - Schrape, Oliver A1 - Stamenkovic, Zoran A1 - Krstić, Miloš A1 - Ilic, Stefan A1 - Ristic, Goran A1 - Jaksic, Aleksandar A1 - Vasovic, Nikola A1 - Duane, Russell A1 - Palma, Alberto J. A1 - Lallena, Antonio M. A1 - Carvajal, Miguel A. T1 - A design concept for radiation hardened RADFET readout system for space applications JF - Microprocessors and microsystems N2 - 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. KW - RADFET KW - Radiation hardness KW - Absorbed dose KW - Dose rate KW - Self-adaptive MPSoC Y1 - 2022 U6 - https://doi.org/10.1016/j.micpro.2022.104486 SN - 0141-9331 SN - 1872-9436 VL - 90 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Tavakoli, Hamad A1 - Alirezazadeh, Pendar A1 - Hedayatipour, Ava A1 - Nasib, A. H. Banijamali A1 - Landwehr, Niels T1 - Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks JF - Computers and electronics in agriculture : COMPAG online ; an international journal N2 - In recent years, many efforts have been made to apply image processing techniques for plant leaf identification. However, categorizing leaf images at the cultivar/variety level, because of the very low inter-class variability, is still a challenging task. In this research, we propose an automatic discriminative method based on convolutional neural networks (CNNs) for classifying 12 different cultivars of common beans that belong to three various species. We show that employing advanced loss functions, such as Additive Angular Margin Loss and Large Margin Cosine Loss, instead of the standard softmax loss function for the classification can yield better discrimination between classes and thereby mitigate the problem of low inter-class variability. The method was evaluated by classifying species (level I), cultivars from the same species (level II), and cultivars from different species (level III), based on images from the leaf foreside and backside. The results indicate that the performance of the classification algorithm on the leaf backside image dataset is superior. The maximum mean classification accuracies of 95.86, 91.37 and 86.87% were obtained at the levels I, II and III, respectively. The proposed method outperforms the previous relevant works and provides a reliable approach for plant cultivars identification. KW - Bean KW - Plant identification KW - Digital image analysis KW - VGG16 KW - Loss KW - functions Y1 - 2021 U6 - https://doi.org/10.1016/j.compag.2020.105935 SN - 0168-1699 SN - 1872-7107 VL - 181 PB - Elsevier CY - Amsterdam [u.a.] ER - TY - JOUR A1 - Michallek, Florian A1 - Genske, Ulrich A1 - Niehues, Stefan Markus A1 - Hamm, Bernd A1 - Jahnke, Paul T1 - Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging BT - a phantom study JF - European Radiology N2 - 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. KW - Tomography KW - X-ray computed KW - Phantoms KW - imaging KW - Liver neoplasms KW - Algorithms KW - Reproducibility of results Y1 - 2022 U6 - https://doi.org/10.1007/s00330-022-08592-y SN - 1432-1084 VL - 32 IS - 7 SP - 4587 EP - 4595 PB - Springer CY - New York ER - TY - JOUR A1 - Bandyopadhyay, Soumyadip A1 - Sarkar, Dipankar A1 - Mandal, Chittaranjan A1 - Giese, Holger T1 - Translation validation of coloured Petri net models of programs on integers JF - Acta informatica N2 - 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. Y1 - 2022 U6 - https://doi.org/10.1007/s00236-022-00419-z SN - 0001-5903 SN - 1432-0525 VL - 59 IS - 6 SP - 725 EP - 759 PB - Springer CY - New York ER - TY - JOUR A1 - Andjelković, Marko A1 - Chen, Junchao A1 - Simevski, Aleksandar A1 - Schrape, Oliver A1 - Krstić, Miloš A1 - Kraemer, Rolf T1 - Monitoring of particle count rate and LET variations with pulse stretching inverters JF - IEEE transactions on nuclear science : a publication of the IEEE Nuclear and Plasma Sciences Society N2 - This study investigates the use of pulse stretching (skew-sized) inverters for monitoring the variation of count rate and linear energy transfer (LET) of energetic particles. The basic particle detector is a cascade of two pulse stretching inverters, and the required sensing area is obtained by connecting up to 12 two-inverter cells in parallel and employing the required number of parallel arrays. The incident particles are detected as single-event transients (SETs), whereby the SET count rate denotes the particle count rate, while the SET pulsewidth distribution depicts the LET variations. The advantage of the proposed solution is the possibility to sense the LET variations using fully digital processing logic. SPICE simulations conducted on IHP's 130-nm CMOS technology have shown that the SET pulsewidth varies by approximately 550 ps over the LET range from 1 to 100 MeV center dot cm(2) center dot mg(-1). The proposed detector is intended for triggering the fault-tolerant mechanisms within a self-adaptive multiprocessing system employed in space. It can be implemented as a standalone detector or integrated in the same chip with the target system. KW - Particle detector KW - pulse stretching inverters KW - single-event transient KW - (SET) count rate KW - SET pulsewidth distribution Y1 - 2021 U6 - https://doi.org/10.1109/TNS.2021.3076400 SN - 0018-9499 SN - 1558-1578 VL - 68 IS - 8 SP - 1772 EP - 1781 PB - Institute of Electrical and Electronics Engineers CY - New York, NY ER - TY - JOUR A1 - Chen, Junchao A1 - Lange, Thomas A1 - Andjelkovic, Marko A1 - Simevski, Aleksandar A1 - Lu, Li A1 - Krstić, Miloš T1 - Solar particle event and single event upset prediction from SRAM-based monitor and supervised machine learning JF - IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers N2 - 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. KW - Machine learning KW - Single event upsets KW - Random access memory KW - monitoring KW - machine learning algorithms KW - predictive models KW - space missions KW - solar particle event KW - single event upset KW - machine learning KW - online learning KW - hardware accelerator KW - reliability KW - self-adaptive multiprocessing system Y1 - 2022 U6 - https://doi.org/10.1109/TETC.2022.3147376 SN - 2168-6750 VL - 10 IS - 2 SP - 564 EP - 580 PB - Institute of Electrical and Electronics Engineers CY - [New York, NY] ER - TY - JOUR A1 - Brede, Nuria A1 - Botta, Nicola T1 - On the correctness of monadic backward induction JF - Journal of functional programming N2 - In control theory, to solve a finite-horizon sequential decision problem (SDP) commonly means to find a list of decision rules that result in an optimal expected total reward (or cost) when taking a given number of decision steps. SDPs are routinely solved using Bellman's backward induction. Textbook authors (e.g. Bertsekas or Puterman) typically give more or less formal proofs to show that the backward induction algorithm is correct as solution method for deterministic and stochastic SDPs. Botta, Jansson and Ionescu propose a generic framework for finite horizon, monadic SDPs together with a monadic version of backward induction for solving such SDPs. In monadic SDPs, the monad captures a generic notion of uncertainty, while a generic measure function aggregates rewards. In the present paper, we define a notion of correctness for monadic SDPs and identify three conditions that allow us to prove a correctness result for monadic backward induction that is comparable to textbook correctness proofs for ordinary backward induction. The conditions that we impose are fairly general and can be cast in category-theoretical terms using the notion of Eilenberg-Moore algebra. They hold in familiar settings like those of deterministic or stochastic SDPs, but we also give examples in which they fail. Our results show that backward induction can safely be employed for a broader class of SDPs than usually treated in textbooks. However, they also rule out certain instances that were considered admissible in the context of Botta et al. 's generic framework. Our development is formalised in Idris as an extension of the Botta et al. framework and the sources are available as supplementary material. Y1 - 2021 U6 - https://doi.org/10.1017/S0956796821000228 SN - 1469-7653 SN - 0956-7968 VL - 31 PB - Cambridge University Press CY - Cambridge ER - TY - JOUR A1 - Marco Figuera, Ramiro A1 - Riedel, Christian A1 - Rossi, Angelo Pio A1 - Unnithan, Vikram T1 - Depth to diameter analysis on small simple craters at the lunar south pole - possible implications for ice harboring JF - Remote sensing N2 - In this paper, we present a study comparing the depth to diameter (d/D) ratio of small simple craters (200-1000 m) of an area between -88.5 degrees to -90 degrees latitude at the lunar south pole containing Permanent Shadowed Regions (PSRs) versus craters without PSRs. As PSRs can reach temperatures of 110 K and are capable of harboring volatiles, especially water ice, we analyzed the relationship of depth versus diameter ratios and its possible implications for harboring water ice. Variations in the d/D ratios can also be caused by other processes such as degradation, isostatic adjustment, or differences in surface properties. The conducted d/D ratio analysis suggests that a differentiation between craters containing PSRs versus craters without PSRs occurs. Thus, a possible direct relation between d/D ratio, PSRs, and water ice harboring might exist. Our results suggest that differences in the target's surface properties may explain the obtained results. The resulting d/D ratios of craters with PSRs can help to select target areas for future In-Situ Resource Utilization (ISRU) missions. KW - craters KW - lunar exploration KW - ice harboring Y1 - 2022 U6 - https://doi.org/10.3390/rs14030450 SN - 2072-4292 VL - 14 IS - 3 PB - MDPI CY - Basel ER - TY - JOUR A1 - Gautam, Khem Raj A1 - Zhang, Guoqiang A1 - Landwehr, Niels A1 - Adolphs, Julian T1 - Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building JF - Computers and electronics in agriculture : COMPAG online ; an international journal N2 - In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building's setpoint deviation improved for 87% of time, on average, by 0.53 degrees C compared to the current deviations. KW - Animal building KW - Natural ventilation KW - Automatically controlled windows KW - Machine learning KW - Optimization Y1 - 2021 U6 - https://doi.org/10.1016/j.compag.2021.106259 SN - 0168-1699 SN - 1872-7107 VL - 187 PB - Elsevier Science CY - Amsterdam [u.a.] ER -