China
Africa
Explore chapters and articles related to this topic
Perspectives on the History of Computing
Published in José López Soriano, Maximizing Benefits from IT Project Management, 2016
The popularity of the personal computer market, growth with a diversification of needs and applications, as well as the progressive dependence on information systems by companies and governments have produced two different but related effects on computers: The first effect is the concentration of hardware platforms into a small number of hardware architectures, with a technological oligopoly supplying computer processing chips to all computer manufacturers around the world. Central processor manufacturers have now been reduced to a number that is small compared to the diversity of existing computer designs and technologies. Today, the best known of these current manufacturers is Intel, maker of the Pentium processor series. In many ways, Intel has been responsible for establishing many of the standards.The second effect is a similar concentration of software platforms, which has been facilitated by the standardization of hardware platforms.
Solving Past Challenges
Published in David W. Richerson, William E. Lee, Modern Ceramic Engineering, 2018
David W. Richerson, William E. Lee
Due to the IC, the electronics industry grew dramatically. This drove IC companies to invest heavily in continued research and development.18 Processing technology advanced to 1000 nm in 1985 and 800 nm in 1989. The number of transistors on a chip increased to around 275,000 and the speed to 33 MHz. By 1992, a CPU with 800 nm resolution, 1.4 million transistors, and 50 MHz was marketed making possible the notebook computer. In 1993, the Pentium P5 was introduced with 3.1 million transistors in the IC, 66 MHz speed and 4 GB memory.
A computational journey in the true north
Published in International Journal of Parallel, Emergent and Distributed Systems, 2020
I went from a personal computer based on an Intel Pentium processor, to a Sun workstation running the Unix (and then the Solaris) operating system. I currently use a LENOVO ThinkPad laptop, a Microsoft Surface, and an Apple iMac. I used to travel to conferences laden with a stack of acetate transparencies on which my talk was inscribed. Then I moved to digital slide presentations and carried the talk on my computer. Shortly thereafter, I only needed to take a USB stick on which the talk was stored. Now, all I have to do is put the presentation online and travel hands free! My first digital camera in 1997, was a Kodak DC210; it had a resolution of one Megapixel. I now carry a SONY α 7Rii which has a resolution of 42.4 Megapixels. Incidentally, I was one of the first users of a computer tablet: I owned a WACOM tablet that I used in my digital dark room.
Agriculture Crop Suitability Prediction Using Rough Set on Intuitionistic Fuzzy Approximation Space and Neural Network
Published in Fuzzy Information and Engineering, 2019
Experimental analysis has been carried out to get the efficiency of the proposed model, RSIFASANN. The experiments were conducted with a computer having Intel Pentium Processor, 8GB RAM, Windows 10 operating system and MATLAB R2008a. For analysis purpose, data are collected from Krishi Vigyan Kendra (KVK), Vellore, India. The data for 4799 villages were collected. But after careful observation, it is identified that 2193 villages are having agriculture crop production as their main occupation. The intuitionistic fuzzy proximity relation is employed on whole data for getting almost equivalence classes. This phase changes the quantitative information system to qualitative information system. Further, the qualitative data set of 2193 objects are validated with the training model. Additionally, we have chosen a model which integrates Bayesian classification and RSFAS (BCRSFAS) [12]. Also, the proposed model is compared with the previous work of hybridising RSFAS with Neural network as (RSFASANN). We have randomly selected 220 objects and predicted the decision using BCRSFAS and the proposed model RSIFASANN. Further, the number of objects is randomly increased by 220. The classification accuracy against both the models was checked. The process is repeated till the whole data set of 2193 objects. The results obtained are presented in Table 9. The average accuracy obtained by the proposed model RSIFASANN is 93.7. The accuracy of model RSIFASANN is higher than the accuracy of RSFASANN and the accuracy of RSFASANN is higher than BCRSFAS.
e-DMDAV: A new privacy preserving algorithm for wearable enterprise information systems
Published in Enterprise Information Systems, 2018
Zhenjiang Zhang, Xiaoni Wang, Lorna Uden, Peng Zhang, Yingsi Zhao
In this paper, we used an Intel Pentium 4 3.1GHz CPU, 512MB RAM, Win 7professional operating system and Mathworks Matlab R2012a to perform the simulations. Two data sets were tested in the experiment. A data set contains 1,000 volunteers’ personal information, including 15 attributes, such as student ID, name, sex, age, date of birth, etc. Five of them were quasi-identifiers attributes and set one of the numeric quasi-identifiers attributes was used as the sensitive attribute. The B data set contains the information about 800 companies in Tarragona gathered in 1995, including 12 quasi-identifiers attributes, and one of the nonnumeric quasi-identifiers attributes was used as a sensitive attribute. e is the normalized limiting factor of sensitive attributes. k is the parameter for the number of records in each cluster.