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Semiglobal matching is an accurate stereo depth estimation algorithm, whereas implementing the high-throughput architecture has been challenging due to the inherent recursion on inter-pixel cost aggregation. Especially, the computation on horizontal scan pass is the critical path causing the throughput bottleneck. In this paper, we propose a new cluster-wise cost aggregation algorithm and its optimized architecture that enables to pipeline the inter-pixel aggregation and parallelize the scanline-level disparity computation. The proposed approach is performed not on every pixel but on each group of pixels, which significantly alleviates the timing constraint for the recursion. The disparity values at shifted multiple pixel positions are concurrently computed within a single clock period. We also propose the memory reduction scheme selecting a tiny number of informative values, which achieves 96% memory reduction compared to the straightforward approach storing overall values. The system-on-chip-based tiled processing scheme is employed, which allows the implementation without an external memory. The proposed architecture computes a depth map with 128 disparity levels at 103 frames per second on a full HD image on the Zynq ultrascale+ MPSoC platform, thus providing 2.6 times faster performance with a comparable accuracy compared to the state-of-the-art 8-path semiglobal matching implementation.

Accurate stereo matching requires a large amount of memory at a high bandwidth, which restricts its use in resource-limited systems such as mobile devices. This problem is compounded by the recent trend of applications requiring significantly high pixel resolution and disparity levels. To alleviate this, we present a memory-efficient and robust stereo matching algorithm. For cost aggregation, we employ the semiglobal parametric approach, which significantly reduces the memory bandwidth by representing the costs of all disparities as a Gaussian mixture model. All costs on multiple paths in an image are aggregated by updating the Gaussian parameters. The aggregation is performed during the scanning in the forward and backward directions. To reduce the amount of memory for the intermediate results during the forward scan, we suggest to store only the Gaussian parameters which contribute significantly to the final disparity selection. We also propose a method to enhance the overall procedure through a learning-based confidence measure. The random forest framework is used to train various features which are extracted from the cost and intensity profile. The experimental results on KITTI dataset show that the proposed method reduces the memory requirement to less than 3 percent of that of semiglobal matching (SGM) while providing a robust depth map compared to those of state-of-the-art SGM-based algorithms.

Depth sensing has been widely used in many applications over the past several decades. However, conventional depth sensing platforms are not suitable for mobile devices due to their large size and computation complexity. In this paper, we propose a compact system for depth and image sensing with offset pixel apertures (OPA). Specifically, we design an enhanced depth map processing (EDMP) and an OPA-dedicated image signal processing (OISP). The EDMP significantly improves the depth quality on excessively dark or light regions, greatly mitigates false depth aggregation at object boundaries, and increases depth precision with a very small overhead. Shot-noise-modeling and two-pass cost aggregation significantly reduce depth errors with reasonable hardware costs. Also, the OISP provides primary image processing while using white pixels to further improve color quality over conventional image sensors. Both EDMP and OISP are integrated on a single-chip. Our system shares the common hardware engine through time-multiplexing, taking advantage of the temporal margin caused by the sparsity of the OPA pixels. Compared to state-of-the-art OPA system, the proposed system reduces the depth error by 71.79%, increases the depth precision by 5.1 times, and reduces the angular error of color image by 54.08% on average.

The acquisition of MDR1-mediated chemoresistance poses a major obstacle to the success of conventional chemotherapeutic agents. HSF1 is also involved in chemoresistance, and several studies have demonstrated the relationship between HSF1 and MDR1 but without any consistent results. Paclitaxel- and doxorubicin-resistant cancer cells showed higher expression of MDR1 and HSF1. Depletion of HSF1 decreased mdr1 expression at mRNA level, and HSF1 directly interacted with the promoter site of mdr1, suggesting its role as a transcriptional regulator of MDR1. Phosphorylation of Ser303/307, which was involved in protein stability of HSF1 by FBXW7-mediated degradation, was found to be important for transcriptional activation of mdr1. Drug-resistant cells showed decreased expression of FBXW7, which was mediated by the activation of ERK1/2, thus indicating that over-activation of ERK1/2 in drug-resistant cells decreased FBXW7 protein stability, which finally inhibited protein degradation of pHSF1 at Ser303/307. There was a positive correlation between immunofluorescence data of pHSF1 at Ser303/307 and MDR1 in carcinogen-induced rat mammary tumors and human lung cancers. These findings identified the post-translational mechanisms of HSF1 transcription in MDR1 regulation of drug resistance development.

Due to the increasing demand for 3D applications, development of novel depth-sensing cameras is being actively pursued. However, most of these cameras still face the challenge of high energy consumption and slow speed in the depth extraction process. This becomes a serious bottleneck in embedded implementations where real-time performance is required, constrained by power and area. This work proposes Offset Aperture (OA) camera, a new hardware architecture for fast, low-energy, and low-complexity depth extraction. Optimal implementations of pre-processing, cost-volume generation and cost-aggregation are presented. The whole depth-extraction pipeline has been implemented on a Field Programmable Gate Array (FPGA). Overall, a mere 2.8% of bad classification was achieved with the proposed system. Also, the proposed system can process 37 VGA frames per second while consuming 0.224 μJ/pixel. High accuracy, speed and low energy consumption of the proposed OA architecture make it suitable for embedded applications.

We described the synthesis and characterization of magnetic Fe3O4 nanoparticle (NP)-embedded phase change material (PCM) nanocapsules (Mag-PCM) based on a paraffin core and polyurea shell (paraffin@polyurea). The Mag-PCM nanocapsules were synthesized via interfacial polycondensation between tolylene diisocyanate (TDI) and ethylene diamine (EDA) with various concentrations (0, 2.2, 3.7, and 4.6 wt%) of Fe3O4 nanoparticles. FTIR and TGA analyses confirmed the successful preparation of Mag-PCM nanocapsules. TEM and SEM images revealed that most of the Fe3O4 NPs were localized in the polyurea shell layer due to the Pickering effect. Laser flash (LFA) and DSC analyses demonstrated that the embedment of Fe3O4 NPs could increase the thermal conductivity of PCM nanocapsules and reduce the supercooling of the paraffin between bulk and nano-size confinement conditions.

Depth sensing is an active area of research in imaging technology. Here, we use a dual-aperture system to infer depth from a single image based on the principle of depth from defocus (DFD). Dual-aperture camera includes a small all-pass aperture (which allows all light through the aperture) and a larger RGB-pass aperture (which allows visible light only). IR image captured through the smaller aperture is sharper than the RGB image captured through the large aperture. Since the difference of blurriness between two images is dependent on the actual distance, using a dual-aperture camera provides an opportunity to estimate depth of a scene. Measuring the absolute blur size is difficult, since it is affected by illuminant’s spectral distribution, noise, specular highlight, vignetting, etc. By using a dual-aperture camera, however, the relative blurriness can be measured in a robust way. In this article,, a detailed description of extracting depth using a dual-aperture camera is provided which includes procedures for fixing each of artifacts that degrade the depth quality based on DFD. Experimental results confirm the improved depth extraction by employing the aforementioned schemes.

Numerous depth extraction techniques have been proposed in the past. However, the utility of these techniques is limited as they typically require multiple imaging units, bulky platforms for computation, cannot achieve high speed and are computationally expensive. To counter the above challenges, a sensor with Offset Pixel Apertures (OPA) has been recently proposed. However, a working system for depth extraction with the OPA sensor has not been discussed. In this paper, we propose the first such system for depth extraction using the OPA sensor. We also propose a dedicated hardware implementation for the proposed system, named as the Depth Map Processor (DMP). The DMP can provide depth at 30 frames per second at 1920 × 1080 resolution with 31 disparity levels. Furthermore, the proposed DMP has low power consumption as for the aforementioned speed and resolution it only requires 290.76 mW. The proposed system makes it an ideal choice for depth extraction systems in constrained environments.

Background subtraction is widely used in battery-operated surveillance system. In the system, memory requirement and computational complexity are important issues due to a constraint of the hardware resource. We propose memory-efficient and low computational complexity background subtraction algorithm based on multi-background registration (MBR) technique algorithm.

Protein pattern has played an important role in biosensors, bioMEMS, tissue engineering, fundamental studies of cell biology, and basic proteomics research. Here, we developed a straightforward and effective protein patterning technique using macroporous poly(2-hydroxyethyl methacrylate) (PHEMA) hydrogel micropatterns as a three-dimensional (3D) template for protein immobilization. Micropatterns of macroporous hydrogels with inverse opal structures were prepared on poly(ethylene glycol) (PEG)-coated silicon substrates by combining a colloidal crystal templating method with photopatterning. The resultant inverse opal hydrogel (IOH) micropatterns were modified with 3-aminopropyltriethoxysilane using the hydroxyl groups in PHEMA for the covalent immobilization of proteins. Proteins were selectively immobilized only on the hydrogel micropatterns, while the PEG regions served as an effective barrier to protein adsorption. Because of their highly ordered and interconnected 3D macroporous structures and large internal surface areas, protein loading in the IOH micropattern was about six times greater than that on a non-porous hydrogel micropattern, which consequently improved the protein activity. The porosity of the hydrogel micropatterns could be controlled using different sizes of colloidal nanoparticles, and using smaller nanoparticles produced hydrogel micropatterns with higher protein loading capacities and activities. To demonstrate the potential use of IOH micropatterns in biosensor systems, biotin was micropatterned on the hydrogels and the specific binding of streptavidin was successfully assayed using IOH micropatterns with better fluorescence signals and sensitivity than that of the corresponding non-porous hydrogel micropatterns.