Dynamics of perpendicular recording heads

3D modeling and inductance measurements were used to design an ultra-high frequency perpendicular system. Kerr microscopy and spin-stand experiments with focused ion beam (FI-B) trimmed perpendicular heads and perpendicular media directly verified the high frequency concepts

Design of low-density parity-check codes for magnetic recording channels.

A technique for designing low-density parity-check (LDPC) error correcting codes for use with the partial-response channels commonly used in magnetic recording is presented. This technique combines the well-known density evolution method of Richardson and Urbanke for analyzing the performance of the LDPC decoder with a newly developed method for doing density evolution analysis of the Bahl-Cocke-Jelinek-Raviv (BCJR) channel decoder to predict the performance of LDPC codes in systems that employ both LDPC and BCJR decoders, and to search for good codes. We present examples of codes that perform 0.3dB to 0.5dB better than the regular column weight three codes employed in previous work.A new algorithm is also presented, which we call "MTR enforcement". Typical magnetic recording systems employ not just an error correcting code, but also some form of run-length-limited code or maximum-transition-run (MTR) code. The MTR enforcement algorithm allows us to exploit the added redundancy imposed by the MTR code to increase performance over that of a magnetic recording system which does not employ the MTR enforcer. We show a gain of approximately 0.5dB from the MTR enforcer in a typical magnetic recording system. We also discuss methods of doing so-called "soft-error estimates", which attempt to extrapolate the bit-error-rate (BER) curve from Monte Carlo simulations down below the limits for which the traditional BER results are valid. The recent work by Yedidia on generalizations of the belief propagation algorithm is discussed, and we consider problems that arise in using this generalized belief propagation method for decoding LDPC codes

Towards Tamper-Evident Storage on Patterned Media

We propose a tamper-evident storage system based on probe storage with a patterned magnetic medium. This medium supports normal read/write operations by out-of-plane magnetisation of individual magnetic dots. We report on measurements showing that in principle the medium also supports a separate class of write-once operation that destroys the out-of-plane magnetisation property of the dots irreversibly by precise local heating. We discuss the main issues of designing a tamper-evident storage device and file system using the properties of the medium

Modeling of long-time thermal magnetization decay in interacting granular magnetic materials

We present a general method to evaluate the long-time magnetization decay in granular magnetic systems. The method is based on Arrhenius-Neel kinetics with the evaluation of the energy barriers in a multidimensional space. To establish a possible reversal mode, we suggest the use of Metropolis Monte Carlo and for the mode statistical sampling-the kinetic Monte Carlo criteria. The examples considered include long-time magnetization decay in CoCrPt low-magnetization longitudinal recording media and in a collection of Co particles with different concentrations

A Step Toward AI Tools for Quality Control and Musicological Analysis of Digitized Analogue Recordings: Recognition of Audio Tape Equalizations

Historical analogue audio documents are indissolubly linked to their physical carriers on which they are recorded. Because of their short life expectancy these documents have to be digitized. During this process, the document may be altered with the result that the digital copy is not reliable from the authenticity point of view. This happens because digitization process is not completely automatized and sometimes it is influenced by human subjective choices. Artificial intelligence can help operators to avoid errors, enhancing reliability and accuracy, and becoming the base for quality control tools. Furthermore, this kind of algorithms could be part of new instruments aiming to ease and to enrich musicological studies. This work focuses the attention on the equalization recognition problem in the audio tape recording field. The results presented in this paper, highlight that, using machine learning algorithms, is possible to recognize the pre-emphasis equalization used to record an audio tape