A novel approach for quality control system using sensor fusion of infrared and visual image processing for laser sealing of food containers

This paper presents a new mechatronic approach of using infrared thermography combined with image processing for the quality control of a laser sealing process for food containers. The suggested approach uses an on-line infrared system to assess the heat distribution within the container seal in order to guarantee the integrity of the process. Visual image processing is then used for quality assurance to guarantee optimum sealing. The results described in this paper show examples of the capability of the condition monitoring system to detect faults in the sealing process. The results found indicate that the suggested approach could form an effective quality control and assurance system

Apical endosomes isolated from kidney collecting duct principal cells lack subunits of the proton pumping ATPase.

Endocytic vesicles that are involved in the vasopressin-stimulated recycling of water channels to and from the apical membrane of kidney collecting duct principal cells were isolated from rat renal papilla by differential and Percoll density gradient centrifugation. Fluorescence quenching measurements showed that the isolated vesicles maintained a high, HgCl2-sensitive water permeability, consistent with the presence of vasopressin-sensitive water channels. They did not, however, exhibit ATP-dependent luminal acidification, nor any N-ethylmaleimide-sensitive ATPase activity, properties that are characteristic of most acidic endosomal compartments. Western blotting with specific antibodies showed that the 31- and 70-kD cytoplasmically oriented subunits of the vacuolar proton pump were not detectable in these apical endosomes from the papilla, whereas they were present in endosomes prepared in parallel from the cortex. In contrast, the 56-kD subunit of the proton pump was abundant in papillary endosomes, and was localized at the apical pole of principal cells by immunocytochemistry. Finally, an antibody that recognizes the 16-kD transmembrane subunit of oat tonoplast ATPase cross-reacted with a distinct 16-kD band in cortical endosomes, but no 16-kD band was detectable in endosomes from the papilla. This antibody also recognized a 16-kD band in affinity-purified H+ ATPase preparations from bovine kidney medulla. Therefore, early endosomes derived from the apical plasma membrane of collecting duct principal cells fail to acidify because they lack functionally important subunits of a vacuolar-type proton pumping ATPase, including the 16-kD transmembrane domain that serves as the proton-conducting channel, and the 70-kD cytoplasmic subunit that contains the ATPase catalytic site. This specialized, non-acidic early endosomal compartment appears to be involved primarily in the hormonally induced recycling of water channels to and from the apical plasma membrane of vasopressin-sensitive cells in the kidney collecting duct

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

Linear optical quantum computation with imperfect entangled photon-pair sources and inefficient non-photon-number-resolving detectors

We propose a scheme for efficient cluster state quantum computation by using imperfect polarization-entangled photon-pair sources, linear optical elements and inefficient non-photon-number-resolving detectors. The efficiency threshold for loss tolerance in our scheme requires the product of source and detector efficiencies should be >1/2 - the best known figure. This figure applies to uncorrelated loss. We further find that the loss threshold is unaffected by correlated loss in the photon pair source. Our approach sheds new light on efficient linear optical quantum computation with imperfect experimental conditions.Comment: 5 pages, 2 figure

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

Temperature dependence of the conductivity of the electronic crystal

We study the temperature dependence of the conductivity of the 2D electronic solid. In realistic samples, a domain structure forms in the solid and each domain randomly orients in the absence of the in-plane field. At higher temperature, the electron transport is governed by thermal activation form of $\sigma_{xx}(T)\propto e^{-\Delta_0/k_BT}$. The impurities will localize the electron states along the edges of the crystal domains. At sufficient low temperature, another transport mechanism called Mott's variable range hopping mechanism, similar to that in a disorder insulator takes effect. We show that as the temperature decreases, a crossover from the fixed range hopping of the transport to the variable range hopping of transport in the 2D electron system may be experimentally observed.Comment: 4 pages,1 figure

Matrix Pencils and Entanglement Classification

In this paper, we study pure state entanglement in systems of dimension $2\otimes m\otimes n$. Two states are considered equivalent if they can be reversibly converted from one to the other with a nonzero probability using only local quantum resources and classical communication (SLOCC). We introduce a connection between entanglement manipulations in these systems and the well-studied theory of matrix pencils. All previous attempts to study general SLOCC equivalence in such systems have relied on somewhat contrived techniques which fail to reveal the elegant structure of the problem that can be seen from the matrix pencil approach. Based on this method, we report the first polynomial-time algorithm for deciding when two $2\otimes m\otimes n$ states are SLOCC equivalent. Besides recovering the previously known 26 distinct SLOCC equivalence classes in $2\otimes 3\otimes n$ systems, we also determine the hierarchy between these classes