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Circuit complexity problems of integrated programmable cellular neural networksPorra, Veikko; Paasio, Ari; Halonen, KariBEC : Baltic Electronics Conference : proceedings of the 4th Biennial Conference, October 9-14, 1994, Tallinn (Estonia). 21994 / p. 411-416: ill https://www.ester.ee/record=b2150914*est

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Classification of cross-sections for vascular skeleton extraction using convolutional neural networksLidayová, Kristína; Gupta, Anindya; Frimmel, Hans; Sintorn, Ida-Maria; Bengtsson, Ewert; Smedby, ÖrjanMedical Image : Understanding and Analysis, 21st Annual Conference, MIUA 2017 Edinburgh, UK, July 11–13, 2017 : Proceedings2017 / p. 182-194 https://doi.org/10.1007/978-3-319-60964-5_16 Conference proceedings at Scopus Article at Scopus Article at WOS

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CNN-Transformer Hybrid Model Towards Automated Droplet Image Quality Assessment of Portable Imaging Flow CytometerAfrin, Fariha; Ndubuisi Ezechukwu, Dismas; Le Moullec, Yannick; Pardy, Tamas; Rang, Toomas; Koel, AntsIEEE EUROCON 2025 - 21st International Conference on Smart Technologies2025 / 6 p https://doi.org//10.1109/EUROCON64445.2025.11073334

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Microﬂuidic Droplet Detection, Classiﬁcation and Quality Assessment for Embedded Flow Cytometry Systems = Tilkade tuvastamine mikroﬂuidikas, nende klassiﬁtseerimine ja kvaliteedi hindamine sardsetes voolutsütomeetria süsteemides

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Combined method to visualize and reduce dimensionality of the financial data setsKirt, ToomasDatabases and information systems : proceedings of the Fifth International Baltic Conference : Baltic DB & IS 2002 : Tallinn, June 3-6, 2002. Vol. 22002 / p. 255-262 : ill

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Combining Digital Twin and Genetic Algorithms to Quantify the Effect of Urban and Microenvironmental Factors on Thermal Comfort in Residential BuildingsJahanbakhsh, Maliheh; Kravchenko, Ilia; Ferrantelli, AndreaProceedings of the 15th REHVA HVAC World Congress - CLIMA 20252026 / p. 174-183 http://doi.org/10.1007/978-3-032-06810-1_18

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31

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Comment on "Solitons in the Heimburg-Jackson model of sound propagation in lipid bilayers are enabled by dispersion of a stiff membrane" by M. Drab et al.Peets, Tanel; Tamm, Kert; Engelbrecht, JüriThe European physical journal E2023 / art. 34 https://doi.org/10.1140/epje/s10189-023-00299-2 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

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A compact 16*16 CMOS cellular neural network chipPaasio, Ari; Dawidziuk, Adam; Porra, VeikkoBEC'96 : the 5th Biennial Baltic Electronics Conference, October 7-11, 1996, Tallinn, Estonia : proceedings1996 / p. 171-174: ill

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A comparative analysis of symbolic and deep learning based RDFS materializationTraagel, Mart; Carneiro Alves de Lima, Bruno Rucy; Cabral Pinheiro, Victor HenriqueAICCC '23: Proceedings of the 2023 6th artificial intelligence and cloud computing conference2023 / p. 60-65 https://doi.org/10.1145/3639592.3639601

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Composing graph theory and deep neural networks to evaluate SEU type soft error effectsBalakrishnan, Aneesh; Lange, Thomas; Glorieux, Maximilien; Alexandrescu, Dan; Jenihhin, Maksim9th Mediterranean Conference on Embedded Computing (MECO'2020), Budva, Montenegro, 8-11 June 20202020 https://doi.org/10.1109/MECO49872.2020.9134279

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Related publications 1

A synthetic, hierarchical approach for modelling and managing complex systems' quality and reliability = Sünteetiline, hierarhiline lähenemine keerukate süsteemide kvaliteedi ja töökindluse modelleerimiseks ja haldamiseks

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Constrained intelligent frequency control in an AC Microgrid : an Online reinforcement learning based PID tuning approachNosrati, Komeil; Tepljakov, Aleksei; Petlenkov, Eduard; Škiparev, Vjatšeslav; Belikov, Juri; Levron, Yoash2023 IEEE Power & Energy Society General Meeting (PESGM)2023 / 5 p. : ill https://doi.org/10.1109/PESGM52003.2023.10252482

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Virtual inertia control of microgrids using deep reinforcement learning methods = Mikrovõrkude virtuaalse inertsi juhtimine sügava stiimulõppe meetoditega

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Contrastive Learning for Lane Detection via cross-similarityZoljodi, Ali; Abadijou, Sadegh; Alibeigi, Mina; Daneshtalab, MasoudPattern Recognition Letters2024 / p. 175-183 : ill https://doi.org/10.1016/j.patrec.2024.08.007 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

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Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM imagesGupta, Anindya; Suveer, Amit; Lindblad, Joakim; Dragomir, Anca; Sintorn, Ida-Maria; Sladoje, NatašaImage Analysis, 20th Scandinavian Conference, SCIA 2017, Tromsø, Norway, June 12–14, 2017 : proceedings, Part I2017 / p. 407-418 : ill https://doi.org/10.1007/978-3-319-59126-1_34 Conference proceedings at Scopus Article at Scopus Article at WOS

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Correction to: Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining (BMC Bioinformatics, (2023), 24, 1, (488), 10.1186/s12859-023-05593-6)Djeddi, Warith Eddine; Hermi, Khalil; Ben Yahia, Sadok; Diallo, GayoBMC Bioinformatics2024 / art. 289 https://doi.org/10.1186/s12859-024-05905-4 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

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Cost-effective fault tolerance for CNNs using parameter vulnerability based hardening and pruningAhmadilivani, Mohammad Hasan; Mousavi, Seyedhamidreza; Raik, Jaan; Daneshtalab, Masoud; Jenihhin, Maksim2024 IEEE 30th International Symposium on On-line Testing and Robust System Design (IOLTS) : IOLTS 2024 : July 03rd-05th 2024, Rennes, Brittany, France2024 / 7 p https://doi.org/10.1109/IOLTS60994.2024.10616072 Article at Scopus Article at WOS

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Related publications 1

Assessment and Enhancement of Hardware Reliability for Deep Neural Networks = Riistvara töökindluse hindamine ja täiustamine süvanärvivõrkude jaoks

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Custom simplified machine learning algorithms for fault diagnosis in electrical machinesRaja, Hadi Ashraf; Asad, Bilal; Vaimann, Toomas; Kallaste, Ants; Rassõlkin, Anton; Belahcen, AnouarDiagnostika '22 : 2022 International Conference on Diagnostics in Electrical Engineering : Conference proceedings2022 / 4 p. https://doi.org/10.1109/Diagnostika55131.2022.9905174

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Related publications 1

IoT based tools and methods for electrical machine diagnostics = Asjade interneti põhised tööriistad ja meetodid elektrimasinate diagnostikaks

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DASS: Differentiable architecture search for sparse neural networksMousavi, Hamid; Loni, Mohammad; Alibeigi, Mina; Daneshtalab, MasoudACM transactions on embedded computing systems2023 / art. 105, 21 p. : ill https://doi.org/10.1145/3609385 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

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Deep Learning methodology for charging management applications in battery cells based on Neural NetworksZequera, Rolando Antonio Gilbert; Rjabtšikov, Viktor; Rassõlkin, Anton; Vaimann, Toomas; Kallaste, AntsIEEE Transactions on Intelligent Vehicles2025 / p. 668-682 https://doi.org/10.1109/TIV.2024.3417216

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Artificial Intelligence-Based Predictive Analytics for Battery Energy Storage Systems in Electric Vehicle Applications = Tehisintellektil põhinev ennustav andmeanalüüs akupõhiste energiasalvestussüsteemide jaoks elektrisõidukites

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Deep padding and alignment strategies for irregular multivariate clinical time seriesBignoumba, Nzamba; Ben Yahia, Sadok; Mellouli, NedraProcedia Computer Science2024 / p. 3275 - 3284 https://doi.org/10.1016/j.procs.2024.09.312 Conference proceedings at Scopus Article at Scopus

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DeepAxe : a framework for exploration of approximation and reliability trade-offs in DNN acceleratorsTaheri, Mahdi; Riazati, Mohamad; Ahmadilivani, Mohammad Hasan; Jenihhin, Maksim; Daneshtalab, Masoud; Raik, Jaan; Sjödin, Mikael; Lisper, Björn2023 24th International Symposium on Quality Electronic Design (ISQED)2023 / 8 p. : ill https://doi.org/10.1109/ISQED57927.2023.10129353

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Related publications 1

Methods for reliability assessment and enhancement of deep neural networks hardware accelerators = Süvanärvivõrkude riistvara kiirendite töökindluse hindamine ja täiustamine

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DeepFlexiHLS : Deep Neural Network Flexible High-Level Synthesis Directive GeneratorRiazati, Mohammad; Daneshtalab, Masoud; Sjödin, Mikael; Lisper, BjörnRiazati, M.; Daneshtalab, M.; Sjodin, M.; Lisper, B.2022 / p. 1-6 https://doi.org/10.1109/NorCAS57515.2022.9934617

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DeepHLS: A complete toolchain for automatic synthesis of deep neural networks to FPGARiazati, Mohammad; Daneshtalab, Masoud; Sjodin, Mikael; Lisper, BjornICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, November 23-25, 2020, Virtual Conference : Proceedings2020 / 4 p https://doi.org/10.1109/ICECS49266.2020.9294881

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DeepVigor: VulnerabIlity Value RanGes and FactORs for DNNs’ Reliability AssessmentAhmadilivani, Mohammad Hasan; Taheri, Mahdi; Raik, Jaan; Daneshtalab, Masoud; Jenihhin, MaksimIEEE European Test Symposium (ETS) : Venice, Italy, 22-26 May 2023 : proceedings2023 / 6 p. : ill https://doi.org/10.1109/ETS56758.2023.10174133

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DeepVigor+: Scalable and Accurate Semi-Analytical Fault Resilience Analysis for Deep Neural NetworksAhmadilivani, Mohammad Hasan; Raik, Jaan; Daneshtalab, Masoud; Jenihhin, MaksimarXiv.org2024 / 14 p. : ill https://doi.org/10.48550/arXiv.2410.15742

journal article

Related publications 1

Assessment and Enhancement of Hardware Reliability for Deep Neural Networks = Riistvara töökindluse hindamine ja täiustamine süvanärvivõrkude jaoks

49

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DenseDisp: Resource-Aware Disparity Map Estimation by Compressing Siamese Neural ArchitectureLoni, Mohammad; Zoljodi, Ali; Maier, Daniel; Majd, Amin; Daneshtalab, Masoud; Sjödin, Mikael; Juurlink, Ben H.H.; Akbari, Reza2020 IEEE Congress on Evolutionary Computation (CEC) : conference proceedings2020 / 8 p https://doi.org/10.1109/CEC48606.2020.9185611

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Design of car frontal protection system using neural networks and genetic algorithmMajak, Jüri; Pohlak, Meelis; Eerme, Martin; Velsker, TarmoMechanika2012 / p. 453-460 : ill https://mechanika.ktu.lt/index.php/Mech/article/view/2325

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