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.

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.

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.

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.

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.

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.

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.

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.

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).