Prediction-based Scheme Research Articles

Standard and Specific Compression Techniques for DNA Microarray Images

We review the state of the art in DNA microarray image compression and provide original comparisons between standard and microarray-specific compression techniques that validate and expand previous work. First, we describe the most relevant approaches published in the literature and classify them according to the stage of the typical image compression process where each approach makes its contribution, and then we summarize the compression results reported for these microarray-specific image compression schemes. In a set of experiments conducted for this paper, we obtain new results for several popular image coding techniques that include the most recent coding standards. Prediction-based schemes CALIC and JPEG-LS are the best-performing standard compressors, but are improved upon by the best microarray-specific technique, Battiato’s CNN-based scheme.

Prediction-based watermarking schemes using ahead/post AC prediction

This study presents two predicted-based watermarking schemes, namely ahead AC-predicted watermarking (AAPW) and post AC-predicted watermarking (PAPW), by embedding information into low frequency AC coefficients of discrete cosine transform (DCT). The proposed methods utilize the DC values of the neighboring blocks to predict the AC coefficients of the center block. The low frequency AC coefficients are modified to carry watermark information. The least mean squares (LMS) is employed to yield the intermediate filters for cooperating with the neighboring DC coefficients to predict the original AC coefficients. During the LMS filter training, the training blocks are classified into different categories according to their texture angles and variances. The classified trained filter sets are then used to predict the AC coefficients even more precisely. As documented in the experimental results, the image quality and the embedded capacity of the proposed schemes are superior to former methods in the literature. Moreover, many attacks are addressed to show the robustness of the proposed methods.

Spectrum Handoff for Cognitive Radio Systems Based on Prediction Considering Cross-Layer Optimization

The spectrum handoff problem for cognitive radio systems is considered in this paper. The secondary users (SUs) can only opportunistically access the spectrum holes, i.e. the frequency channels unoccupied by the primary users (PUs). As long as a PU appears, SUs have to vacate the channel to avoid interference to PUs and switch to another available channel. In this paper, a prediction-based spectrum handoff scheme is proposed to reduce the negative effect (both the interference to PUs and the service block of SUs) during the switching time. In the proposed scheme, a hidden Markov model is used to predict the occupancy of a frequency channel. By estimating the state of the model in the next time instant, we can predict whether the frequency channel will be occupied by PUs or not. As a cross-layer design, the spectrum sensing performance parameters false alarm probability and missing detection probability are taken into account to enhance accuracy of the channel occupancy prediction. The proposed scheme will react on the spectrum sensing algorithm parameters while the spectrum handoff performance is significantly affected by them. The interference to the PUs could be reduced obviously by adapting the proposed spectrum handoff scheme, associated with a potential increase of switch delay of SUs. It will also be helpful for SUs to save broadband scan time and prefer an appropriate objective channel so as to avoid service block. Numerical results demonstrate the above performance improvement by using this prediction-based scheme.

DCTC: Dynamic Convoy Tree-Based Collaboration for Target Tracking in Sensor Networks

Most existing work on sensor networks concentrates on finding efficient ways to forward data from the information source to the data centers, and not much work has been done on collecting local data and generating the data report. This paper studies this issue by proposing techniques to detect and track a mobile target. We introduce the concept of dynamic convoy tree-based collaboration, and formalize it as a multiple objective optimization problem which needs to find a convoy tree sequence with high tree coverage and low energy consumption. We propose an optimal solution which achieves 100% coverage and minimizes the energy consumption under certain ideal situations. Considering the real constraints of a sensor network, we propose several practical implementations: the conservative scheme and the prediction-based scheme for tree expansion and pruning; the sequential and the localized reconfiguration schemes for tree reconfiguration. Extensive experiments are conducted to compare the practical implementations and the optimal solution. The results show that the prediction-based scheme outperforms the conservative scheme and it can achieve similar coverage and energy consumption to the optimal solution. The experiments also show that the localized reconfiguration scheme outperforms the sequential reconfiguration scheme when the node density is high, and the trend is reversed when the node density is low.

An improved strategy for automated electron microscopic tomography

A prediction-based scheme is proposed and implemented for automated electron microscopic tomography. By assuming that the sample follows a simple geometric rotation and that the optical system can be characterized in terms of an offset between the optical and mechanical axes, it is found that the image movement in the x, y, and z directions due to stage tilt can be dynamically predicted with desired accuracy (15 nm in x– y position and 100 nm in focus). Thus, the microscope optical system (beam/image shift and focus) can be automatically adjusted to compensate for the predicted image movement prior to taking the projected image at each tilt angle. As a consequence, it is not necessary to either record additional images for tracking and focusing during the course of data collections or to spend valuable setup time in a lengthy pre-calibration of stage motions. Furthermore, this scheme is also found to tolerate a significant degree of non-eucentricity and to be quite robust in the collection of regular and cryo low-dose images on thin or thick samples even at magnifications greater than 62 000× and angular step as large as 10°. For interested users the software can be freely downloaded for non-profit use at http://www.msg.ucsf.edu/tomography.

An efficient spatial prediction-based image compression scheme

We have designed a spatial prediction-based image-compression scheme. The proposed scheme consists of two phases: the prediction phase and the quantization phase. In the prediction phase, a hierarchical structure among pixels in the image is built. Following the constructed hierarchical structure, the neighboring pixels are utilized to predict every central pixel. The prediction scheme generates an image map which indicates the prediction errors. The structure of the resulting image map is very similar to the result of a discrete wavelet transform. Thus, most quantization methods of wavelet or subband image-compression algorithms can be followed in our scheme directly to yield good compression performance. In the quantization phase, we design a multilevel threshold scheme to further enhance the result of SPIHT by taking the significance of the pixel values and the hierarchical levels into account. Furthermore, the proposed scheme can be realized by only a few integer additions and bit shifts. Simulation results indicate that the visual quality of the designed efficient spatial prediction-based image compression scheme is competitive with JPEG. All the above features make the designed image-compression scheme beneficial to the applications of real-time and wireless transmission in low-computational power environments.

Location management techniques for mobile systems

Abstract Locating users in mobile environment is an essential problem in PCS that becomes more challenging as the network size increases and the user population grows. In third generation mobile systems, the signaling traffic and processing overhead of location updates is expected to grow tremendously leading to poor performance. Therefore location management schemes should aim at reducing the cost of updates. Yet, the lookup delay should be kept minimum. This paper aims at classifying the various approaches used for location management of mobile users by grouping them into two main categories. The first category comprises techniques that focused on reducing the cost of looking up a user and adjusted the update process accordingly. These are classified into replication , caching and selective paging techniques. The second category consists of techniques that focused on reducing the cost of updates and maneuvered the update policy to reduce the lookup cost by informing the system with the maximum possible information about user's mobility. This could be achieved by the use of statistic collection, estimation or prediction processes. We classify those schemes into three main classes: schemes based on forwarding pointers , learning-based schemes and prediction-based schemes. By investigating the technical significance of each class a new direction for future research is proposed which favors the second category of location techniques and emphasis the importance of adopting suitable learning and prediction techniques to optimize the overall location cost.