6081651
A Novel Lossless Compression for Hyperspectral Images by Adaptive Classified Arithmetic Coding in Wavelet Domain
In this paper, we propose a lossless compression algorithm for hyperspectral images; it is based on the adaptive classified arithmetic coding in wavelet domain and the adaptive spectral band reordering algorithm. The adaptive classified scheme divides each of the residual images after wavelet transform into different classes, and then the adaptive arithmetic coding is performed for each of the classes. This classified coding scheme saves a lot of coding bits. The adaptive spectral band reordering algorithm finds out the nearly best reference band for each of the bands, so the spectral correlation is better used. Combining these two algorithms makes full use of the characteristics of hyperspectral images. Experiments show that our method is capable of providing a high compression performance.
18278402
Hyperspectral images lossless compression by a novel three-dimensional wavelet coding
In this paper, we present a 3D hyperspectral images lossless compression algorithm, which is based on the asymmetric 3D quincunx structured wavelet transform (A3D-QWT) and the adaptive classification coding. A3D-QWT is a simpler 3D transform compared to the classical asymmetric 3D pyramidal wavelet structure (A3D-DWT) with nearly equal entropy; furthermore the spectral correlation after this quincunx structured transform is higher, which is useful to the adaptive classification coding. The adaptive classification coding can make full use of not only the spatial correlation but also the spectral correlation characteristics of hyperspectral images. Experiments show that our method is capable of providing a higher compression performance for hyperspectral images.
46004277
Improved Blind Decodings of STBC with Unknown and Known Channel Correlation to Transmitter
The subspace algorithm can be utilized for the blind detection of space-time block codes (STBC) without knowledge of channel state information (CSI) both at the transmitter and receiver. However, its performance degrades when the channels are correlated. In this letter, we analyze the impact of channel correlation from the orthogonality loss between the transmit signal subspace (TSS) and the statistical noise subspace (SNS). Based on the decoding property of the subspace algorithm, we propose a revised detection in favor of the channel correlation matrix (CCM) only known to the receiver. Then, a joint transmit-receive preprocessing scheme is derived to obtain a further performance improvement when the CCM is available both at the transmitter and receiver. Analysis and simulation results indicate that the proposed methods can significantly improve the blind detection performance of STBC over the correlated channels.
9440354
Sequential Proximity-Based Clustering for Telecommunication Network Alarm Correlation
Alarm correlation for fault management in large telecommunication networks demands scalable and reliable algorithms. In this paper, we propose a clustering based alarm correlation approach using sequential proximity between alarms. We define two novel distance metrics appropriate for measuring similarity between alarm sequences obtained from interval-based division: 1) the two-digit binary metric that values the occurrences of two alarms in neighboring intervals to tolerate the false separation of alarms due to interval-based alarm sequence division, and 2) the sequential ordering-based distance metric that considers the time of arrival for different alarms within the same interval. We validate both metrics by applying them with hierarchical clustering using real-world cellular network alarm data. The efficacy of the proposed sequential proximity based alarm clustering is demonstrated through a comparative study with existing similarity metrics.
26234026
Application of neuro-fuzzy control for satellite AOCS
The attitude control of a future satellite is facing the challenge of its uncertain model because of flexural bodies coupling to the center body. This paper proposes a neuro-fuzzy approach to control the uncertain dynamics and disturbance. The flexible model of solar array is extended to the satellite attitude equation. To overcome the variation of internal parameters and external disturbances, three adaptive neuro-fuzzy controllers are applied to realize the attitude and orbit control system (AOCS) on the base of Proportional-Integral-Differential (PID) controllers. A least square estimation method is combined with gradient descent learning in order to optimize the learning error universally during train procedure. Simulation results show the neuro-fuzzy controllers have good performance to abate influence from disturbance, fadeless vibration and the change of the satellite's inertia parameters.
18186703
Multimodal Registration using the Discrete Wavelet Frame Transform
Image registration is a critical step in medical image analysis. In this paper, a novel image registration method based on the discrete wavelet frame transform (DWFT) and the sum of absolute distance (SAD) method is proposed. First, the multimodal images are decomposed by DWFT, which is shift-invariant compared to traditional dyadic wavelet transforms. Then the energy maps are computed from the details sub-band images. Finally, genetic algorithm (GA) is adopted to obtain the minimum SAD between the two energy maps. The proposed method is tested on 50 pairs of two-dimensional T1-weighted and T2-weighted modal images. Experimental results demonstrated that the proposed method can achieve high accuracy in the rigid transformation case
12408412
Watermarking for Authentication of LZ-77 Compressed Documents
Fragile Watermarking by exploiting the multiplicity of encoding of a file using LZ77- based compressed method (we called"LZS-77"), has been proposed recently as a new method of document authentication. It allows one to hide enough information within the compressed document to warrant its authenticity. However, the watermark embedding will degrade the compression performance. In this paper, some variations have been made to the LZS-77 algorithm to reduce the compression degradation. The experimental results show that our algorithm (we called"LZSC-77"), compared to the LZS-77 algorithm, can improve compression rates on many files of the Cargary corpus and Canterbury corpus.
5864048
The nature of knowledge and its influence on knowledge sharing practice: experiences from building the MACROS system
This study investigates how the interactive influences of the nature of knowledge and multiple organizational and technological factors - trust, leadership, incentives and issues, group size and variety, implementation strategy, and technology - facilitated and/or impeded the knowledge sharing processes. Using a case study approach, the research focuses on the modifying effects of four characteristics of knowledge- codifiability, context-embeddedness, practice-embeddedness, and dynamics- on the processes and outcomes of knowledge sharing in a case of building the multi-purpose access for customer relations & operational support (MACROS) system involving multiple organizations, divisions, and geographically separated offices. The case results suggest that modifying effects occurred along at least three dimensions- codifiability, context-embeddedness, and practice-embeddedness. The levels of codifiability appeared to dictate the implementation strategy; reduced context-embeddedness allowed for more effective group coordination; reduced context-embeddedness also enhanced trust; codified knowledge is more effective than uncodified knowledge in demonstrating concrete incentives; and technology interacts with context-embeddedness and practice-embeddedness. The results of this study have theoretical and practical implications for a larger set of problems encountered in sizable organizations. More specifically, even though the relevance of the nature of knowledge has been widely acknowledged by I. Nonaka, et. al. (2001), it is not always clear how it comes into play. This study conceptualized the nature of knowledge as a modifying variable, and the results provide a new and more comprehensive framework for investigating the relevance of the nature of knowledge in knowledge management research.