Microcomputer Architecture and Programming
Gerard Ian Prudhomme (Contributor)
Available / dispatched within 1 - 4 weeks
Initial computer software was hard wired. Programs were difficult to implement. To re-create computer software required altering the equipment by hand, that began to take quite a long time with a myriad of possible mistakes. Memory space was merely useful for holding information. The phrase micro usually describes the CPU of a microcomputer, including the arithmetic logic unit ( ALU ). Von Neumann recommended that information and also software ought to be kept with each other in memory space. This is now known as a Von Neumann structure. Software, and also information, are symbolized in memory space in the same manner. The primary complaint directed towards this approach is that safety issues may occur while directions may be exploited as though these were information, and also vice versa. A CPU is linked to memory space and also I/O by buses which carry data between the units. It is typically used on one LSI microchip. Microcomputers utilize a time clock, a microprocessor unit ( MPU ), interfaces to memory space, and also exterior input/output units. The first chapter views a novel parallelization strategy, aligning sequences in architectures with many cores. Chapter 2 analyzes a uniform transmission line. Chapter 3 scrutinizes a hybrid single-electron device. Chapter 4 suggests a parallel architecture of stochastic-logic pulse-based blocks. Chapter 5 puts emphasis on Satellite-based Automatic Information Systems. Chapter 6 evaluates robotic work cell design. Chapter 7 peruses microfluidic chips. Chapter 8 probes advanced data modeling and analysis. Chapter 9 evaluates digital information in geological fields. Chapter 10 gives an outline of clustering algorithms. Chapter 11 figures out mobile phone location data. Chapter 12 analyzes heterogeneous information networks. Chapter 13 views deep learning theory for data-driven research. Chapter 14 features formalization and reasoning of topological relations. Chapter 15 scrutinizes assessments of data quality. Chapter 16 is designed to develop social network information tracing. Chapter 17 scrutinizes floating car data and urban-scale traffic monitoring. Chapter 18 deals with remote sensing data. Chapter 19 explains coreference resolution. Chapter 20 analyzes Sequential Pattern Mining. Chapter 21 studies a graphics processing unit (GPU)-based k-NNG algorithm.
About the Author
Gerard I. Prudhomme has a graduate degree (M.S.) for Computer Science from University College London (UCL). He has also worked as a software programmer and tech writer for different Fortune 500 companies, and studied at UCL, Harvard, and Oxford.
- Contributor: Gerard Ian Prudhomme
- Imprint: Arcler Education Inc
- ISBN13: 9781680944662
- Number of Pages: 294
- Packaged Dimensions: 152x229mm
- Format: Hardback
- Publisher: Arcler Education Inc
- Release Date: 2016-11-30
- Binding: Hardback
- Biography: Gerard I. Prudhomme has a graduate degree (M.S.) for Computer Science from University College London (UCL). He has also worked as a software programmer and tech writer for different Fortune 500 companies, and studied at UCL, Harvard, and Oxford.
We hope you are delighted with everything you buy from us. However, if you are not, we will refund or replace your order up to 30 days after purchase. Terms and exclusions apply; find out more from our Returns and Refunds Policy.