No abstract

No abstract

No abstract

No abstract

No abstract

The recording performance of a new prototype magnetic tape based on perpendicularly oriented strontium ferrite particles is investigated using a 29 nm wide tunneling magnetoresistive reader. At a linear density of 702 kbpi, a post-detection byte-error rate of 2.8e-2 is demonstrated based on measured recording data and a software read channel. The read channel uses a 64-state implementation of an extended version of a data-dependent noise-predictive maximum-likelihood detection scheme that tracks the first and second order statistics of the data-dependent noise. At the demonstrated post-detection byte-error rate, a post-error-correction-coding byte-error rate of less than 1e-20 can be achieved using an iterative decoding architecture. To facilitate aggressive track-density scaling, we made multiple advances in the area of track following. First, we developed a new timing-based servo pattern and implemented a novel quad channel averaging scheme. Second, we developed a new field programmable gate array prototyping platform to enable the implementation of quad channel averaging. Third, we enhanced our low disturbance tape transport with a pair of 20 mm diameter air bearing tape guides and a prototype track-following actuator. Fourth, we developed a novel low friction tape head and finally, we designed a set of tape speed optimized track-following controllers using the model-based H∞ design framework. Combining these technologies, we achieved a position error signal (PES) characterized by a standard deviation ≤ 3.18 nm over a tape speed range of 1.2 to 4.1 m/s. This magnitude of PES in combination with a 29 nm wide reader enables reliable recording at a track width of 56.2 nm corresponding to a track density of 451.9 ktpi, for an equivalent areal density of 317.3 Gb/in 2 .

Abstract. We consider the problem of real-time sensing and tracking the location of a moving cart in an indoor environment. To this end, we propose to combine position information obtained from an inertial navigation system (INS) and a short-range wireless reference system that can be embedded into a future"network of things". The data produced by the INS lead to accurate short-term position estimates, but due to the drifts inherent to the system, these estimates perform loosely after some time. To solve this problem, we also generate estimates with a wireless reference system. These radio-based estimates can be used as accurate long-term position estimates because their accuracy improves over time as the channel fading can be averaged out. We use a data fusion algorithm based on Kalman filtering with forward/backward smoothing to optimally combine the short-and long-term position estimates. We have implemented this localization system in a real-time testbed. The measurement results, which we obtained using the proposed method, show considerable improvements in accuracy of the location estimates.

No abstract