scholarly journals Forecasting Technology Development Based on Patent Information.

2000 ◽  
Vol 43 (1) ◽  
pp. 205-211 ◽  
Author(s):  
Akira OKAWA ◽  
Yuji FURUKAWA
2019 ◽  
Vol 951 (9) ◽  
pp. 25-39
Author(s):  
V.V. Zabavnikov ◽  
A.N. Kobiakov ◽  
S.V. Kovalev

Informational and analytical studying patent documentation shows the patenting situation either in general in a specific technological area or the patent activity of innovation entities, taking temporal dynamics and the territorial basis into account. Patent-information investigation was carried out in order to get acquainted with the level of photogrammetry technology development and determine its current application areas. Statistical and intellectual patent document text analysis was the basis for relevant data array grouped in 8680 patent families’ creation. The prepared report contains a graphical display of selected patent documents array, related to research topic, analytical and statistical processing. The level of inventive activity was assessed; the world patenting dynamics and location in this technical field were considered. The main groups on the International Patent Classification, as well as the main technological directions, where technical solutions related to the object of study to be patented, are identified. Information on the leading applicants/ patent holders in this technical field is provided; the list of the most cited patent documents is considered.


Author(s):  
Simon Thomas

Trends in the technology development of very large scale integrated circuits (VLSI) have been in the direction of higher density of components with smaller dimensions. The scaling down of device dimensions has been not only laterally but also in depth. Such efforts in miniaturization bring with them new developments in materials and processing. Successful implementation of these efforts is, to a large extent, dependent on the proper understanding of the material properties, process technologies and reliability issues, through adequate analytical studies. The analytical instrumentation technology has, fortunately, kept pace with the basic requirements of devices with lateral dimensions in the micron/ submicron range and depths of the order of nonometers. Often, newer analytical techniques have emerged or the more conventional techniques have been adapted to meet the more stringent requirements. As such, a variety of analytical techniques are available today to aid an analyst in the efforts of VLSI process evaluation. Generally such analytical efforts are divided into the characterization of materials, evaluation of processing steps and the analysis of failures.


2012 ◽  
pp. 117-131 ◽  
Author(s):  
O. Golichenko

The problems of multifold increase of technological potential of developing countries are considered in the article. To solve them, i.e. to organize effectively tapping into global knowledge and their absorption, the performance of two diffusion channels is considered: open knowledge transfer and commercial knowledge transfer. The models of technological catching-up are investigated. Two of them are found to give an opportunity of effective use of international competition and global technology knowledge as a driver of technology development.


Author(s):  
M. Jeyanthi ◽  
C. Velayutham

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.


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