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In this study, we consider a quantum channel that may help to discriminate quantum states, by which we may provide the solution for the unsolved problem of discrimination of quantum states. For the quantum channel to help the problem of discriminating given quantum states, the quantum channel should not change the guessing probability as well as the optimal measurement in minimum error discrimination of those quantum states. Therefore, we investigate a quantum channel that preserves an optimal measurement and the guessing probability with minimum error discrimination of quantum states. Then, we show that the channel does not exist in every set of quantum states but does exist when the channel and an optimal measurement commute with each other. The effectiveness of the channel can be shown more explicitly when the discrimination of linearly independent quantum states, which has not been solved yet, is considered. When we apply the channel to linearly independent quantum states, the discrimination of quantum states can be transformed into a problem in which the number of quantum states in discrimination is reduced, and we can provide a means of obtaining an answer to an unsolved quantum state discrimination. As an unsolved problem of discrimination of quantum states, we consider an ensemble of three pure states with real coefficients, whose subsets contain Unitary symmetry-called partial symmetry. By applying our strategy, we can provide an analytic result for optimal discrimination to the ensemble of three pure states with partial symmetry. [ABSTRACT FROM AUTHOR]

Real-time monitoring of suspended particulate matter (PM) has become essential in daily life due to the adverse effects of long-term exposure to PMs on human health and ecosystems. However, conventional techniques for measuring micro-scale particulates commonly require expensive instruments. In this study, a smartphone-based device is developed for real-time monitoring of suspended PMs by integrating a smartphone-based digital holographic microscopy (S-DHM) and deep learning algorithms. The proposed S-DHM-based PM monitoring device is composed of affordable commercial optical components and a smartphone. Overall procedures including digital image processing, deep learning training, and correction process are optimized to minimize the prediction error and computational cost. The proposed device can rapidly measure the mass concentrations of coarse and fine PMs from holographic speckle patterns of suspended polydisperse PMs in water with measurement errors of 22.8 ± 18.1% and 13.5 ± 9.8%, respectively. With further advances in data acquisition and deep learning training, this study would contribute to the development of hand-held devices for monitoring polydisperse non-spherical pollutants suspended in various media. [ABSTRACT FROM AUTHOR]

In this study, we present the first actual working system that can project content onto a tunnel wall from a moving subway train so that passengers can enjoy the display of digital content through a train window. Our stand-alone system can be easily deployed to existing trains because it does not assume any specific type of interface requiring data transfer with the train. To effectively estimate the position of the train in a tunnel, we propose counting sleepers, which are installed at regular interval along the railway, using a distance sensor. In the preprocessing step, the side depth variation along the tunnel is stored in synchronization with the train location in a tunnel profile. The tunnel profile is constructed using pointclouds captured by a depth camera installed next to the projector. The tunnel profile is used to identify projectable sections that will not contain too much interference by possible occluders. The tunnel profile is also used to retrieve the depth at a specific location so that a properly warped content can be projected for viewing by passengers through the window when the train is moving at runtime. Here, we show that the proposed system can operate on an actual train and evaluate the quality of the projection results through simulation. [ABSTRACT FROM AUTHOR]