ADAPTIVE AND SECURE DISTRIBUTED SOURCE CODING FOR VIDEO AND IMAGE COMPRESSION

Distributed Video Coding (DVC) is rapidly gaining popularity as a low cost, robust video coding solution, that reduces video encoding complexity. DVC is built on Distributed Source Coding (DSC) principles where correlation between sources to be compressed is exploited at the decoder side. In the case of DVC, a current frame available only at the encoder is estimated at the decoder with side information (SI) generated from other frames available at the decoder. The inter-frame correlation in DVC is then explored at the decoder based on the received syndromes of Wyner-Ziv (WZ) frame and SI frame. However, the ultimate decoding performances of DVC are based on the assumption that the perfect knowledge of correlation statistic between WZ and SI frames should be available at decoder. Therefore, the ability of obtaining a good statistical correlation estimate is becoming increasingly important in practical DVC implementations.Generally, the existing correlation estimation methods in DVC can be classified into two main types: online estimation where estimation starts before decoding and on-the-fly (OTF) estimation where estimation can be refined iteratively during decoding. As potential changes between frames might be unpredictable or dynamical, OTF estimation methods usually outperforms online estimation techniques with the cost of increased decoding complexity.In order to exploit the robustness of DVC code designs, I integrate particle filtering with standard belief propagation decoding for inference on one joint factor graph to estimate correlation among source and side information. Correlation estimation is performed OTF as it is carried out jointly with decoding of the graph-based DSC code. Moreover, I demonstrate our proposed scheme within state-of-the-art DVC systems, which are transform-domain based with a feedback channel for rate adaptation. Experimental results show that our proposed system gives a significant performance improvement compared to the benchmark state-of-the-art DISCOVER codec (including correlation estimation) and the case without dynamic particle filtering tracking, due to improved knowledge of timely correlation statistics via the combination of joint bit-plane decoding and particle-based BP tracking.Although sampling (e.g., particle filtering) based OTF correlation advances performances of DVC, it also introduces significant computational overhead and results in the decoding delay of DVC. Therefore, I tackle this difficulty through a low complexity adaptive DVC scheme using the deterministic approximate inference, where correlation estimation is also performed OTF as it is carried out jointly with decoding of the factor graph-based DVC code but with much lower complexity. The proposed adaptive DVC scheme is based on expectation propagation (EP), which generally offers better tradeoff between accuracy and complexity among different deterministic approximate inference methods. Experimental results show that our proposed scheme outperforms the benchmark state-of-the-art DISCOVER codec and other cases without correlation tracking, and achieves comparable decoding performance but with significantly low complexity comparing with sampling method.Finally, I extend the concept of DVC (i.e., exploring inter-frames correlation at the decoder side) to the compression of biomedical imaging data (e.g., CT sequence) in a lossless setup, where each slide of a CT sequence is analogous to a frame of video sequence. Besides compression efficiency, another important concern of biomedical imaging data is the privacy and security. Ideally, biomedical data should be kept in a secure manner (i.e. encrypted).An intuitive way is to compress the encrypted biomedical data directly. Unfortunately, traditional compression algorithms (removing redundancy through exploiting the structure of data) fail to handle encrypted data. The reason is that encrypted data appear to be random and lack the structure in the original data. The "best" practice has been compressing the data before encryption, however, this is not appropriate for privacy related scenarios (e.g., biomedical application), where one wants to process data while keeping them encrypted and safe. In this dissertation, I develop a Secure Privacy-presERving Medical Image CompRessiOn (SUPERMICRO) framework based on DSC, which makes the compression of the encrypted data possible without compromising security and compression efficiency. Our approach guarantees the data transmission and storage in a privacy-preserving manner. I tested our proposed framework on two CT image sequences and compared it with the state-of-the-art JPEG 2000 lossless compression. Experimental results demonstrated that the SUPERMICRO framework provides enhanced security and privacy protection, as well as high compression performance

Why torus-unstable solar filaments experience failed eruption?

To investigate the factors that control the success and/or failure of solar eruptions, we study the magnetic field and 3-Dimensional (3D) configuration of 16 filament eruptions during 2010 July - 2013 February. All these events, i.e., erupted but failed to be ejected to become a coronal mass ejection (CME), are failed eruptions with the filament maximum height exceeding $100 Mm$. The magnetic field of filament source regions is approximated by a potential field extrapolation method. The filament 3D configuration is reconstructed from three vantage points by the observations of STEREO Ahead/Behind and SDO spacecraft. We calculate the decay index at the apex of these failed filaments and find that in 7 cases, their apex decay indexes exceed the theoretical threshold ($n_{crit} = 1.5$) of the torus instability. We further determine the orientation change or rotation angle of each filament top during the eruption. Finally, the distribution of these events in the parameter space of rotation angle versus decay index is established. Four distinct regimes in the parameter space are empirically identified. We find that, all the torus-unstable cases (decay index $n > 1.5$), have a large rotation angles ranging from $50^\circ - 130^\circ$. The possible mechanisms leading to the rotation and failed eruption are discussed. These results imply that, besides the torus instability, the rotation motion during the eruption may also play a significant role in solar eruptions

Characterization of two β-galactosidases LacZ and WspA1 from Nostoc flagelliforme with focus on the latter’s central active region

The identification and characterization of new β-galactosidases will provide diverse candidate enzymes for use in food processing industry. In this study, two β-galactosidases, Nf-LacZ and WspA1, from the terrestrial cyanobacterium Nostoc flagelliforme were heterologously expressed in Escherichia coli, followed by purification and biochemical characterization. Nf-LacZ was characterized to have an optimum activity at 40\ua0\ub0C and pH 6.5, different from that (45\ua0\ub0C and pH 8.0) of WspA1. Two enzymes had a similar Michaelis constant (Km = 0.5\ua0mmol/liter) against the substrate o-nitrophenyl-β-D-galactopyranoside. Their activities could be inhibited by galactostatin bisulfite, with IC50 values of 0.59\ua0\ub5M for Nf-LacZ and 1.18\ua0\ub5M for WspA1, respectively. Gel filtration analysis suggested that the active form of WspA1 was a dimer, while Nf-LacZ was functional as a larger multimer. WspA1 was further characterized by the truncation test, and its minimum central region was found to be from residues 188 to 301, having both the glycosyl hydrolytic and transgalactosylation activities. Finally, transgenic analysis with the GFP reporter protein found that the N-terminus of WspA1 (35 aa) might play a special role in the export of WspA1 from cells. In summary, this study characterized two cyanobacterial β-galactosidases for potential applications in food industry

Disordered hyperuniformity signals functioning and resilience of self-organized vegetation patterns

In harsh environments, organisms may self-organize into spatially patterned systems in various ways. So far, studies of ecosystem spatial self-organization have primarily focused on apparent orders reflected by regular patterns. However, self-organized ecosystems may also have cryptic orders that can be unveiled only through certain quantitative analyses. Here we show that disordered hyperuniformity as a striking class of hidden orders can exist in spatially self-organized vegetation landscapes. By analyzing the high-resolution remotely sensed images across the American drylands, we demonstrate that it is not uncommon to find disordered hyperuniform vegetation states characterized by suppressed density fluctuations at long range. Such long-range hyperuniformity has been documented in a wide range of microscopic systems. Our finding contributes to expanding this domain to accommodate natural landscape ecological systems. We use theoretical modeling to propose that disordered hyperuniform vegetation patterning can arise from three generalized mechanisms prevalent in dryland ecosystems, including (1) critical absorbing states driven by an ecological legacy effect, (2) scale-dependent feedbacks driven by plant-plant facilitation and competition, and (3) density-dependent aggregation driven by plant-sediment feedbacks. Our modeling results also show that disordered hyperuniform patterns can help ecosystems cope with arid conditions with enhanced functioning of soil moisture acquisition. However, this advantage may come at the cost of slower recovery of ecosystem structure upon perturbations. Our work highlights that disordered hyperuniformity as a distinguishable but underexplored ecosystem self-organization state merits systematic studies to better understand its underlying mechanisms, functioning, and resilience.Comment: 34 pages, 6 figures; Supplementary Materials, 19 pages, 10 figures, 2 table

Effect of mangrove restoration on crab burrow density in Luoyangjiang Estuary, China

Background Mangrove restoration seeks to restore or rebuild degraded mangrove systems. The methods of mangrove restoration include ecological projects and restoration-oriented technologies, the latter of which are designed to restore the structure, processes as well as related physical, chemical and biological characteristics of wetlands and to ensure the provision of ecosystem services. As important components of mangrove ecosystem, benthic organisms and crabs play a key role in nutrient cycling. In addition, mangrove restoration, such as vegetation restoration measures, can lead to changes in the benthic faunal communities. This study investigates whether the presence of different mangrove species, age and canopy cover of mangrove communities affect the density of crab burrows. Methods The Luoyangjiang Estuary, in the southeast of Fujian Province, was selected as our research area. A survey, covering 14 sites, was conducted to investigate the impacts of mangrove restoration on the density of crab burrows in four rehabilitated forests with different stand ages and canopy. Results It was found that differences in vegetation types had a large impact on crab density and that the density of crab burrows was lower on exposed beaches (non-mangrove) than under mature Kandelia candel, Aegiceras corniculatum and Avicennia marina communities. In general, the amount of leaf litter and debris on mangrove mudflats was greater than on the beaches as food sources for crabs. Two-factor analysis of variance (ANOVA) shows that changes in mangrove species and age since restoration had different effects on crab burrow density. The effect of canopy cover was highly significant on crab burrow density. Conclusions The results suggest that in the process of mangrove restoration the combined effects of mangrove stand age, canopy cover and other factors should be taken into account. This study further supports the findings of the future scientific research and practice on mangrove restoration and management measures