We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system. 相似文献
Image fusion is an important technique which aims to produce a synthetic result by leveraging the cross information available in the existing data. Sparse Representation (SR) is a powerful signal processing theory used in wide variety of applications like image denoising, compression and fusion. Construction of a proper dictionary with reduced computational efficiency is a major challenge in these applications. Owing to the above criterion, we propose a supervised dictionary learning approach for the fusion algorithm. Initially, gradient information is obtained for each patch of the training data set. Then, the edge strength and information content are measured for the gradient patches. A selection rule is finally employed to select the patches with better focus features for training the over complete dictionary. By the above process, the number of input patches for dictionary training is reduced to a greater extent. At the fusion step, the globally learned dictionary is used to represent the given set of source image patches. Experimental results with various source image pairs demonstrate that the proposed fusion framework gives better visual quality and competes with the existing methodologies quantitatively. 相似文献
Wireless Networks - Unmanned Aerial Vehicles (UAVs) deployed as flying base stations is a promising technology for enhancing the quality of service (QoS) and quick recovery from unexpected damages... 相似文献
International Journal of Information Security - Outsourcing of data is a very common scenario in the present-day world and quite often we need to outsource confidential data whose privacy is of... 相似文献
Fusion of multimodal imaging data supports medical experts with ample information for better disease diagnosis and further clinical investigations. Recently, sparse representation (SR)‐based fusion algorithms has been gaining importance for their high performance. Building a compact, discriminative dictionary with reduced computational effort is a major challenge to these algorithms. Addressing this key issue, we propose an adaptive dictionary learning approach for fusion of multimodal medical images. The proposed approach consists of three steps. First, zero informative patches of source images are discarded by variance computation. Second, the structural information of remaining image patches is evaluated using modified spatial frequency (MSF). Finally, a selection rule is employed to separate the useful informative patches of source images for dictionary learning. At the fusion step, batch‐OMP algorithm is utilized to estimate the sparse coefficients. A novel fusion rule which measures the activity level in both spatial domain and transform domain is adopted to reconstruct the fused image with the sparse vectors and trained dictionary. Experimental results of various medical image pairs and clinical data sets reveal that the proposed fusion algorithm gives better visual quality and competes with existing methodologies both visually and quantitatively. 相似文献
Biocompatible Polysulfone (PSf) hemodialysis membranes were prepared by phase inversion technique using poly (ether-imide)
(PEI) as the modification agent and Polyethylene glycol (PEG-200) as the pore former. The effect of PSf/PEI blend ratio on
the morphology, hydrophilicity, water content, porosity, glass transition temperature, mechanical strength, biocompatibility
and permeation rate of the prepared membranes were studied and were found to be improved significantly by the incorporation
of PEI in the dope solution. The scanning electron microscopy (SEM) studies revealed that, incorporation of PEI resulted in
the formation of spongy sub-layer and increased the connectivity of pores between sub-layer and bottom layer. The water content
and permeation rate of the membranes of PSf/PEI blend membranes were increased considerably indicating the enhancement of
hydrophilicity and it was supported by lower contact angle values of the blend membranes. The existence of single well defined
Tg over entire composition established the compatibility between the components in blend membranes. The biocompatibility of
membranes was investigated through protein adsorption, platelet adhesion and thrombus formation on the membrane surface. Anticoagulant
activity of PSf/PEI blend membranes was evaluated by measuring the activated partial thrombin time (APTT), prothrombin time
(PT), thrombin time (TT) and fibrinogen time (FT). The results revealed that antithrombogenicity of PSf/PEI blend membranes
was increased significantly. The efficiency of these membranes in removal of urea, creatinine and vitamin B12 were studied and found to be improved for blend membranes. Thus, it is worth mentioning to note that, the biocompatible PSf/PEI
blend membranes prepared in this study would offer immense potential in hemodialysis. 相似文献
Every day, people and animals contract debilitating and life threatening diseases due to bites from infected flies, ticks, and mosquitoes. The current methods utilized to fight against these diseases are only partially effective or safe for humans and animals. When it comes to insect vector control, a conceptual paradigm shift is urgently needed. This work proposes a novel synthetic scheme to produce a nanoparticle-pesticide core-shell conjugate to be used as an active agent against arthropod vectors, such as mosquitoes. As a proof of concept, we conjugated nanosilver to the pyrethroid pesticide deltamethrin. First, electron microscopy and Fourier transform infrared spectroscopy verified the presence of a 15 nm nanosilver core surrounded by deltamethrin. Second, when the conjugate was exposed to mosquitoes for a 24 h bioassay, mortality was observed at 9 × 10(-4) M. Silver was detected in the hemolymph of mosquitoes exposed to the conjugate. We concluded that the newly developed nanoconjugate did not inactivate the primary function of the pesticide and was effective in killing mosquitoes at low concentrations. These results demonstrate the potential to use nanoparticle surfaces to kill insects, specifically vectors of human pathogens. 相似文献
The molecular dynamics (MD) simulation of TODGA in n-dodecane shows formation of nanostructures of TODGA aggregates with nitric acid and water. These aggregates are dispersed in dodecane phase or form well defined reverse micelles grown sufficient in size depending on the acid concentration. With increasing nitric acid concentration, aggregation number of TODGA in reverse micelles also increases which, however, is independent of TODGA concentration. Aggregation number rises from 2 to 8 in presence of 0–3.5 M nitric acid in corresponding aqueous phase. The formation of the aggregates explains remarkable acid co-extraction from aqueous phase to organic dodecane phase by TODGA. 相似文献
Image fusion is the process which aims to integrate the relevant and complementary information from a set of images into a single comprehensive image. Sparse representation (SR) is a powerful technique used in a wide variety of applications like denoising, compression and fusion. Building a compact and informative dictionary is the principal challenge in these applications. Hence, we propose a supervised classification based learning technique for the fusion algorithm. As an initial step, each patch of the training data set is pre-classified based on their gradient dominant direction. Then, a dictionary is learned using K-SVD algorithm. With this universal dictionary, sparse coefficients are estimated using greedy OMP algorithm to represent the given set of source images in the dominant direction. Finally, the Euclidean norm is used as a distance measure to reconstruct the fused image. Experimental results on different types of source images demonstrate the effectiveness of the proposed algorithm with conventional methods in terms of visual and quantitative evaluations.