Neural networks-then and now

The emergence of neural networks as a significant subdiscipline with corresponding attempts at application to engineering problems is traced back to the 1960s, when Frank Rosenblatt, a Cornell University psychologist, showed by mathematical analysis, digital computer simulation, and experiments with special-purpose parallel analog systems that neural networks with variable-weight connections could be trained to classify spatial patterns into prespecified categories. In his attempts to provide biologically plausible explanations of the function of the central nervous system, he investigated relatively simple networks that were amenable to analysis and more complex networks whose behavior could be predicted only in terms of gross characteristics. He assembled a sizable group involving theoreticians, experimentalists, technologists, and, later, biologists. His work caught the imagination of the press and led to the wave of febrile activity that subsided at the end of that decade.     

Asymptotic analysis of queues in multiple-access computer networks

The paper considers local-area computer networks with a protocol that supports carrier-sense multiple access with instantaneous conflict detection and is a generalization of slotted ALOHA. An approach based on recurrent processes is applied to analyze the asymptotic behavior of the number of waiting calls under conditions of low retrial rate.Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 54–61, July–August, 1991.     

Fast algorithms for estimating the correlation functions of large signals

A fast algorithm is proposed for estimating the auto- and cross-correlation functions of a large signal. The algorithm is based on the sectioning method by the fast Fourier transform. We determine the optimal length of the portion of data read from external memory into RAM which achieves T_min—a minimum processing time. An estimate of T_min is obtained.Translated from Kibernetika, No. 3, pp. 78–81, May–June, 1991.     

An expert diagnostic system for the MACRO experiment at the GranSasso Laboratory

The maintenance of the MACRO (a Monopoles, Astrophysics and Cosmic Ray Observatory), a large-area detector that will be used to search for rare constituents or phenomena in cosmic radiation penetrating deep underground, is addressed. A real-time expert system for diagnosing detector and data acquisition system anomalies, which is based on the NEXPERT commercial tool, is described. It performs online diagnosis and, if an abnormal condition is identified, takes the appropriate action to reduce the unavailability of the apparatus. The data acquisition system is CAMAC-based, and the sensor modules, which gather the significant values for diagnosis, are implemented in the VME crate     

Seasat over-land scatterometer data. I. Global overview of theKu-band backscatterer coefficients

Statistics on the backscatter coefficient σ⁰ from the Ku-band Seasat-A Satellite Scatterometer (SASS) collected over the world's land surfaces are presented. This spaceborne scatterometer provided data on σ⁰ between latitude 80° S and 80° N at incidence angles up to 70°. The global statistics of vertical (V) and horizontal (H) polarization backscatter coefficients for 10° bands in latitude are presented for incidence angles between 20° and 70° and compared with the Skylab and ground spectrometer results. Global images of the time-averaged V polarization σ⁰ at a 45° incidence angle and its dependence on the incidence angle are presented and compared to a generalized map of the terrain type. Global images of the differences between the V an H polarization backscatter coefficients are presented and discussed. The most inhomogeneous region, which contains the deserts of North Africa and the Arabian Peninsula, is studied in greater detail and compared with the terrain type