The enormous growth of consumer electronic industry is driving the need for achieving ultra-high areal storage densities. Shingled magnetic recording (SMR) is a promising high density storage technique where shingles are created by overlapping tracks for increasing the track per inch (TPI) and achieving higher areal densities. High TPI values, however, introduce sidetrack interference in the equalized output of the center track sensed through the read head. The problem of removing interference from the center track signal can be formulated as estimating pulse shapes of sidetracks assuming sidetracks are decodable. In this paper, we first propose a solution using the correlation method for estimating the pulse shape and the phase-misalignment. The correlation method fails when there is a frequency offset present from sidetracks. Furthermore, we devise a solution based on the least mean square (LMS) algorithm which can provide time varying estimates of the sidetrack pulse shapes and remove the interference from the center track signal. Experimental and simulation results which corroborate the analysis of the proposed algorithm, demonstrate that the proposed algorithm outperforms other techniques and gives the minimum bit error rate (BER). The complexity of the proposed algorithm is low and it can be easily implemented on the hardware.

Power consumption is very critical for portable video applications such as portable videophone and digital camcorder. Motion estimation (ME) in the video encoder requires a huge amount of computation, and hence consumes the largest portion of power. We propose a novel method of reducing power consumption of the ME by adaptively changing the pixel resolution during the computation of the motion vector. The pixel resolution is changed by masking or truncating the least significant bits of the pixel data, which is governed by the bit-rate control mechanism. Experimental results show that on average more than 4 bits ran be truncated without significantly affecting the picture quality. This results in more than 60% reduction in power consumption.

This paper presents a power evaluation framework designedfor estimating power consumption of a new video telephonecompression standard, ITU-H.263, at the system level.A hierarchical,mixed-level simulation environment is built andcycle-accurate power macro-modeling is used for the architecturalpower evaluation.Experimental results show the effectivenessof the proposed framework and models.

Power consumption is very critical for portable video applications such as portable video-phone. Motion estimation in the video encoder requires huge amount of computation and hence consumes the largest portion of power. In this paper we propose a novel method of reducing power consumption of the motion estimation by adaptively changing the pixel resolution during the computation of the motion vector. The pixel resolution is changed by mashing or truncating the LSBs of the pixel data which is governed by an adaptive mechanism. Experimental results show that on average more than 4 bits can be truncated without affecting the picture quality. This results in an average 70% reduction in power consumption.