Many 8-bit and 16-bit microcontrollers feature 10-bitinternal ADCs. A few include 12-bit ADCs, but these oftenhave poor or nonexistent AC specifi cations, and certainlylack the performance to meet the needs of an increasingnumber of applications. The LTC®2366 and its slowerspeed versions offer a high performance alternative, asshown in the AC specifi cations in Table 1. Compare theseguaranteed specifi cations with the ADC built into yourcurrent microcontroller.
Description: FASBIR(Filtered Attribute Subspace based Bagging with Injected Randomness) is a variant of Bagging algorithm, whose purpose is to improve accuracy of local learners, such as kNN, through multi-model perturbing ensemble.
Reference: Z.-H. Zhou and Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.4, pp.725-735.
Description: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP.
Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107.
· Develop clear, readable, well-documented and well-designed programs in the C Programming Language.
· Develop software in the Unix/Linux using tools such as gcc, gdb, and make.
· Locate and interpreting “man pages” applicable to application-level system programming.
· Use the POSIX/Unix API to system functions to manage process and sessions as well as use signals and pipes for inter-process communication.
· Understanding how synchronization might become problematic in light of concurrency.
· Understand how to communicate and cooperate with a project partner.
This manual describes omniidl, the omniORB IDL compiler. It is intended for developers
who wish to write their own IDL compiler back-ends, or to modify existing
ones. It also documents the design of the compiler front-end for those poor souls
who have to track the IDL specification.
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
