Èlektronnoe modelirovanie最新文献

{"title":"Hardware Decompressor Design","authors":"A.M. Sergiyenko, I.V. Mozghovyi","doi":"10.15407/emodel.45.05.113","DOIUrl":"https://doi.org/10.15407/emodel.45.05.113","url":null,"abstract":"The common lossless compression algorithms were analyzed, and the LZW algorithm was selected for the hardware implementation. To express parallelism, this algorithm is represented as a cyclo-dynamic dataflow (CDDF). A hardware synthesis method for designing pipelined datapath is proposed, which optimizes CDDF considering the features of the FPGA primitives and maps it to hardware using VHDL language description. Using this method, an LZW de¬compressor is developed, which exhibits a high performance-to-hardware cost ratio. The de¬com¬¬¬pressor can be utilized in communication channels and other application-specific systems for data loading from memory, generating graphical stencils, and more.","PeriodicalId":474184,"journal":{"name":"Èlektronnoe modelirovanie","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136361172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Two-level Method for Modeling Fluid Movement Using a Lattice Boltzmann Model and a Convolutional Neural Network","authors":"M.A., Novotarskyi, V.A. Kuzmych","doi":"10.15407/emodel.45.05.039","DOIUrl":"https://doi.org/10.15407/emodel.45.05.039","url":null,"abstract":"A new two-level method for modeling fluid movement in closed surfaces is proposed. The metod simulates an unsteady hydrodynamic process and includes two levels of description of the modeling process. The first level supports the development of the process over time and is implemented based on the Boltzmann lattice model. At the second level, for each time layer, based on the obtained velocity field, the pressure distribution is refined by modeling the solution of the Poisson equation in the working area using a convolutional neural network, which is pre-trained on a training data set formed for a given set of typical problems. A method combi¬ning both technologies is proposed, taking into account the compensation of the compressibi¬lity characteristic. The structure and features of neural network training are described. Experiments were conducted on models simulating the human digestive tract in various states. The performance of the developed method is compared with the numerical way of solving the Poisson equation.","PeriodicalId":474184,"journal":{"name":"Èlektronnoe modelirovanie","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136360849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vector-deductive Memory-based Transactions for Fault-as-address Simulation","authors":"W. Gharibi, A. Hahanova, V. Hahanov, S. Chumachenko, E. Litvinova, I. Hahanov","doi":"10.15407/emodel.45.01.003","DOIUrl":"https://doi.org/10.15407/emodel.45.01.003","url":null,"abstract":"The main idea is to create logic-free vector computing, using only read-write transactions on address memory. The strategic goal is to create a deterministic vector-quantum computing using photons for read-write transactions on stable subatomic memory elements. The main task is to implement new vector computing models and methods based on primitive read-write transactions in vector flexible interpretive fault modeling and simulation technology, where data is used as addresses for processing the data itself. The essence of vector computing is read-write transactions on vector data structures in address memory. Vector computing is a computational process based on elementary read-write transactions over cells of binary vectors that are stored in address memory and form a functionality where the input data to be processed is the addresses of these cells. The advantages of a vector universal model for a compact description of ordered processes, phenomena, functions, and structures are defined for the purpose of their parallel analysis. Analytical expressions of logic, which require algorithmically complex calculators, are replaced by output state vectors of elements and digital circuits, focused on the parallelism of register logical procedures on regular data structures. A vector-deductive method for formula synthesis for propagating input lists (data) of faults is proposed, which has a quadratic computational complexity of register operations. A new matrix of deductive vectors has been synthesized, which is characterized by the following properties: compactness, parallel data processing based on a single read-write transaction in memory, elimination of traditional logic from fault simulation procedures, full automation of its synthesis process, and focus on technological solving all problems of technical diagnosis. In the work, the transition to vector logic in the organization of computing and the elimination of traditional logic presented in the form of tables and analytical expressions were carried out. The use of read-write transactions on memory in the absence of a command system focuses the new vector-logic computing towards deterministic quantum architectures based on stable subatomic memory particles.","PeriodicalId":474184,"journal":{"name":"Èlektronnoe modelirovanie","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134964927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}