Comparative study of the ferromagnetic resonance behavior of coupled rectangular and circular Ni[subscript 80]Fe[subscript 20] rings
Comparative study of the ferromagnetic resonance behavior of coupled rectangular and circular Ni80Fe20 rings G. Shimon,1,2 A. O. Adeyeye,1,2,* and C. A. Ross2,3,† 1Department of Electrical and Computer Engineering, 4 Engineering Drive 3, National University of Singapore, Singapore 117576, Singapore 2Singapore-MIT Alliance, 4 Engineering Drive 3, Singapore 117576, Singapore 3Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA (Received 23 September 2013; revised manuscript received 29 November 2013; published 7 January 2014)
Magnetic Memory: Fundamentals and Technology
If you are a semiconductor engineer or a magnetics physicist developing magnetic memory, get the information you need with this, the first book on magnetic memory. From magnetics to the engineering design of memory, this practical book explains key magnetic properties and how they are related to memory performance, characterization methods of magnetic films, and tunneling magnetoresistance effect devices. It also covers memory cell options, array architecture, circuit models, and read-write engineering issues. You'll understand the soft fail nature of magnetic memory, which is very different from that of semiconductor memory, as well as methods to deal with the issue. You'll also get invaluable problem-solving insights from real-world memory case studies. This is an essential book for semiconductor engineers who need to understand magnetics, and for magnetics physicists who work with MRAM. It is also a valuable reference for graduate students working in electronic/magnetic device research.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.