Ray-Space Motion Compensation for Lenslet Plenoptic Video Coding.

Plenoptic images and videos bearing rich information demand a tremendous amount of data storage and high transmission cost. While there has been much study on plenoptic image coding, investigations into plenoptic video coding have been very limited. We investigate the motion compensation (or so-called temporal prediction) for plenoptic video coding from a slightly different perspective by looking at the problem in the ray-space domain instead of in the conventional pixel domain. Here, we develop a novel motion compensation scheme for lenslet video under two sub-cases of ray-space motion, that is, integer ray-space motion and fractional ray-space motion. The proposed new scheme of light field motion-compensated prediction is designed such that it can be easily integrated into well-known video coding techniques such as HEVC. Experimental results compared to relevant existing methods have shown remarkable compression efficiency with an average gain of 20.03% and 21.76% respectively under "Low delayed B " and "Random Access" configurations of HEVC.

Culture-Free Quantification of Bacteria Using Digital Fluorescence Imaging in a Tunable Magnetic Capturing Cartridge for Onsite Food Testing.

Accurate, onsite detection of pathogenic bacteria from food matrices is required to rapidly respond to pathogen outbreaks. However, accurately detecting whole-cell bacteria in large sample volumes without an enrichment step remains a challenge. Therefore, bacterial samples must be concentrated, identified, and quantified. We developed a tunable magnetic capturing cartridge (TMCC) and combined it with a portable digital fluorescence reader for quick, onsite, quantitative detection of Staphylococcus aureus. The TMCC platform integrates an absorption pad impregnated with water-soluble polyvinyl alcohol (PVA) with an injection-molded polycarbonate (PC) plate that has a hard magnet on its back and an acrylonitrile-butadiene-styrene case. An S. aureus-specific antibody conjugated with magnetic nanoparticles was used to concentrate bacteria from a large-volume sample and capture bacteria within the TMCC. The retention time for capturing bacteria on the TMCC was adjusted by controlling the concentration and volume of the PVA solution. Concentrated bacterial samples bound to target-specific aptamer probes conjugated with quantum dots were loaded into the TMCC for a controlled time, followed by attachment of the bacteria to the PC plate and removal of unbound aptamer probes with wash buffer. The captured bacteria were quantified using a digital fluorescence reader equipped with an embedded program that automatically counts fluorescently tagged bacteria. The bacterial count made using the TMCC was comparable to a standard plate count (R2 = 0.9898), with assay sensitivity and specificity of 94.3 and 100%, respectively.

Tensor Cascaded-Rank Minimization in Subspace: A Unified Regime for Hyperspectral Image Low-Level Vision.

Low-rank tensor representation philosophy has enjoyed a reputation in many hyperspectral image (HSI) low-level vision applications, but previous studies often failed to comprehensively exploit the low-rank nature of HSI along different modes in low-dimensional subspace, and unsurprisingly handled only one specific task. To address these challenges, in this paper, we figured out that in addition to the spatial correlation, the spectral dependency of HSI also implicitly exists in the coefficient tensor of its subspace, this crucial dependency that was not fully utilized by previous studies yet can be effectively exploited in a cascaded manner. This led us to propose a unified subspace low-rank learning regime with a new tensor cascaded rank minimization, named STCR, to fully couple the low-rankness of HSI in different domains for various low-level vision tasks. Technically, the high-dimensional HSI was first projected into a low-dimensional tensor subspace, then a novel tensor low-cascaded-rank decomposition was designed to collapse the constructed tensor into three core tensors in succession to more thoroughly exploit the correlations in spatial, nonlocal, and spectral modes of the coefficient tensor. Next, difference continuity-regularization was introduced to learn a basis that more closely approximates the HSI's endmembers. The proposed regime realizes a comprehensive delineation of the self-portrait of HSI tensor. Extensive evaluations conducted with dozens of state-of-the-art (SOTA) baselines on eight datasets verified that the proposed regime is highly effective and robust to typical HSI low-level vision tasks, including denoising, compressive sensing reconstruction, inpainting, and destriping. The source code of our method is released at https://github.com/CX-He/STCR.git.

An Acquisition Method for Visible and Near Infrared Images from Single CMYG Color Filter Array-Based Sensor.

Near-infrared (NIR) images are very useful in many image processing applications, including banknote recognition, vein detection, and surveillance, to name a few. To acquire the NIR image together with visible range signals, an imaging device should be able to simultaneously capture NIR and visible range images. An implementation of such a system having separate sensors for NIR and visible light has practical shortcomings due to its size and hardware cost. To overcome this, a single sensor-based acquisition method is investigated in this paper. The proposed imaging system is equipped with a conventional color filter array of cyan, magenta, yellow, and green, and achieves signal separation by applying a proposed separation matrix which is derived by mathematical modeling of the signal acquisition structure. The elements of the separation matrix are calculated through color space conversion and experimental data. Subsequently, an additional denoising process is implemented to enhance the quality of the separated images. Experimental results show that the proposed method successfully separates the acquired mixed image of visible and near-infrared signals into individual red, green, and blue (RGB) and NIR images. The separation performance of the proposed method is compared to that of related work in terms of the average peak-signal-to-noise-ratio (PSNR) and color distance. The proposed method attains average PSNR value of 37.04 and 33.29 dB, respectively for the separated RGB and NIR images, which is respectively 6.72 and 2.55 dB higher than the work used for comparison.

A novel scheme for essential protein discovery based on multi-source biological information.

Mining essential protein is crucial for discovering the process of cellular organization and viability. At present, there are many computational methods for essential proteins detecting. However, these existing methods only focus on the topological information of the networks and ignore the biological information of proteins, which lead to low accuracy of essential protein identification. Therefore, this paper presents a new essential proteins prediction strategy, called DEP-MSB which integrates a variety of biological information including gene expression profiles, GO annotations, and Domain interaction strength. In order to evaluate the performance of DEP-MSB, we conduct a series of experiments on the yeast PPI network and the experimental results have shown that the proposed algorithm DEP-MSB is more superior to the other existing traditional methods and has obviously improvement in prediction accuracy.

Efficient In-loop Filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC.

The raw video data can be compressed much by the latest video coding standard, high efficiency video coding (HEVC). However, the block-based hybrid coding used in HEVC will incur lots of artifacts in compressed videos, the video quality will be severely influenced. To settle this problem, the in-loop filtering is used in HEVC to eliminate artifacts. Inspired by the success of deep learning, we propose an efficient in-loop filtering algorithm based on the enhanced deep convolutional neural networks (EDCNN) for significantly improving the performance of in-loop filtering in HEVC. Firstly, the problems of traditional convolutional neural networks models, including the normalization method, network learning ability, and loss function, are analyzed. Then, based on the statistical analyses, the EDCNN is proposed for efficiently eliminating the artifacts, which adopts three solutions, including a weighted normalization method, a feature information fusion block, and a precise loss function. Finally, the PSNR enhancement, PSNR smoothness, RD performance, subjective test, and computational complexity/GPU memory consumption are employed as the evaluation criteria, and experimental results show that when compared with the filter in HM16.9, the proposed in-loop filtering algorithm achieves an average of 6.45% BDBR reduction and 0.238 dB BDPSNR gains.

A fully integrated bacterial pathogen detection system based on count-on-a-cartridge platform for rapid, ultrasensitive, highly accurate and culture-free assay.

Rapid, sensitive and accurate point-of-care-testing (POCT) of bacterial load from a variety of samples can help prevent human infections caused by pathogenic bacteria and mitigate their spreading. However, there is an unmet demand for a POCT device that can detect extremely low concentrations of bacteria in raw samples. Herein, we introduce the 'count-on-a-cartridge' (COC) platform for quantitation of the food-borne pathogenic bacteria Staphylococcus aureus. The system comprised of magnetic concentrator, sensing cartridge and fluorescent image reader with a built-in counting algorithm facilitated fluorescent microscopic bacterial enumeration in user-convenient manner with high sensitivity and accuracy within a couple of hours. The analytical performance of this assay is comparable to that of a standard plate count. The COC assay shows a sensitivity of 92.9% and specificity of 100% performed according to global microbiological criteria for S. aureus which is acceptable below 100 CFU/g in the food matrix. This culture-independent, rapid, ultrasensitive and highly accurate COC assay has great potential for places where prompt bacteria surveillance is in high demand.

A smartphone fluorescence imaging-based mobile biosensing system integrated with a passive fluidic control cartridge for minimal user intervention and high accuracy.

A key challenge for realizing mobile device-based on-the-spot environmental biodetection is that a biosensor integrated with a fluid handling sensor cartridge must have acceptable accuracy comparable to that of conventional standard analytical methods. Furthermore, the user interface must be easy to operate, technologically plausible, and concise. Herein, we introduced an advanced smartphone imaging-based fluorescence microscope designed for Hg2+ monitoring by utilizing a biosensor cartridge that reduced user intervention via time-sequenced passive fluid handling. The cartridge also employed a metal-nanostructured plastic substrate for complementing the fluorescence signal output; this helped the realization of high-accuracy detection, in which a ratiometric dual-wavelength detection method was applied. Using 30 samples of Hg2+-spiked wastewater, we showed that our device, which has a detection limit of ∼1 pM, can perform analytical assays accurately. The detection results from our method were in good linearity and agreement with those of conventional standard methods. We conclude that the integration of a simple-to-use biosensor cartridge, fluorescence signal-enhancing substrate, dual-wavelength detection, and quantitative image data processing on a smartphone has great potential to make any population accessible to small-molecule detection, which has been performed in centralized laboratories for environmental monitoring.

A Network Hierarchy-Based method for functional module detection in protein-protein interaction networks.

In the post-genomic era, one of the important tasks is to identify protein complexes and functional modules from high-throughput protein-protein interaction data, so that we can systematically analyze and understand the molecular functions and biological processes of cells. Although a lot of functional module detection studies have been proposed, how to design correctly and efficiently functional modules detection algorithms is still a challenging and important scientific problem in computational biology. In this paper, we present a novel Network Hierarchy-Based method to detect functional modules in PPI networks (named NHB-FMD). NHB-FMD first constructs the hierarchy tree corresponding to the PPI network and then encodes the tree such that genetic algorithm is employed to obtain the hierarchy tree with Maximum Likelihood. After that functional module partitioning is performed based on it and the best partitioning is selected as the result. Experimental results in the real PPI networks have shown that the proposed algorithm not only significantly outperforms the state-of-the-art methods but also can detect protein modules more effectively and accurately.