{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":33,"n_cols":5,"rows_kept":33,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[13,2,13,3],[15,2,15,3],[16,2,16,3],[20,2,20,3],[23,2,23,3],[23,4,23,5],[24,2,24,3],[24,4,24,5],[25,2,25,3],[25,4,25,5],[26,2,26,3],[27,2,27,3],[28,2,28,3],[29,2,29,3],[30,2,30,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910739479503321"]
[null,null,"Autore",null,"Shen Bo"]
[null,null,"Titolo",null,"Nonlinear stochastic systems with incomplete information : filtering and control / / Bo Shen, Zidong Wang, Huisheng Shu"]
[null,null,"Pubbl/distr/stampa",null,"London, : Springer, 2013"]
[null,null,"ISBN",null,"1-299-19715-9"]
[{"$nulls":4},"1-4471-4914-9"]
[null,null,"Edizione",null,"[1st ed. 2013.]"]
[null,null,"Descrizione fisica",null,"1 online resource (xvi, 248 pages) : illustrations (some color)"]
[null,null,"Collana",null,"Gale eBooks"]
[null,null,"Disciplina",null,"519"]
[{"$nulls":4},"519.2"]
[{"$nulls":4},"519.22"]
[{"$nulls":4},"530.1/5"]
[null,null,"Soggetti",null,"Stochastic systems"]
[{"$nulls":4},"Nonlinear theories"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"From the Contents: Quantized H-infinity Control for Nonlinear Stochastic Time-delay Systems with Missing Measurements -- Nonlinear H-infinity Filtering for Discrete-Time Stochastic Systems with Missing Measurements and Randomly Varying Sensor Delays -- Robust H-infinity Filtering with Randomly Occurring Nonlinearities, Quantization Effects and Successive Packet Dropouts -- H-infinity Filtering with Randomly Occurring Sensor Saturations and Missing Measurements -- Distributed H-infinity Consensus Filtering in Sensor Networks with Multiple Missing Measurements: The Finite-Horizon Case."]
[null,null,null,"Sommario/riassunto","Nonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem considered here include: missing measurements; sensor delays and saturation; quantization effects; and signal sampling. Divided into three parts, the text begins with a focus on H∞ filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems. Filtering problems are considered in the second part, and in the third the theory and techniques previously developed are applied to the solution of issues arising in complex networks with the design of sampled-data-based controllers and filters. Among its highlights, the text provides: ·         a unified framework for handling "]
[{"$nulls":4},"filtering and control problems in complex communication networks with limited bandwidth; ·         new concepts such as random sensor and signal saturations for more realistic modeling; and ·         demonstration of the use of techniques such as the Hamilton–Jacobi–Isaacs, difference linear matrix, and parameter-dependent matrix inequalities and sums of squares to handle the computational challenges inherent in these systems. The collection of recent research results presented in Nonlinear Stochastic Processes will be of interest to academic researchers in control and signal processing. Graduate students working with communication networks with lossy information and control of stochastic systems will also benefit from reading the book."]
[null,"2.","Record Nr.",null,"UNINA9910346856603321"]
[null,null,"Autore",null,"Piasini Eugenio"]
[null,null,"Titolo",null,"Information Theory in Neuroscience"]
[null,null,"Pubbl/distr/stampa",null,"MDPI - Multidisciplinary Digital Publishing Institute, 2019"]
[null,null,"ISBN",null,"3-03897-665-2"]
[null,null,"Descrizione fisica",null,"1 online resource (280 p.)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","As the ultimate information processing device, the brain naturally lends itself to being studied with information theory. The application of information theory to neuroscience has spurred the development of principled theories of brain function, and has led to advances in the study of consciousness, as well as to the development of analytical techniques to crack the neural code-that is, to unveil the language used by neurons to encode and process information. In particular, advances in experimental techniques enabling the precise recording and manipulation of neural activity on a large scale now enable for the first time the precise formulation and the quantitative testing of hypotheses about how the brain encodes and transmits the information used for specific functions across areas. This Special Issue presents twelve original contributions on novel approaches in neuroscience using information theory, and on the development of new information theoretic results inspired by problems in neuroscience."]