Characterization and modeling of acousto-optic signal strengths in highly scattering media

Ultrasound optical tomography (UOT) is an imaging technique based on the acousto-optic effect that can perform optical imaging with ultrasound resolution inside turbid media, and is thus interesting for biomedical applications, e.g. for assessing tissue blood oxygenation. In this paper, we present near background free measurements of UOT signal strengths using slow light filter signal detection. We carefully analyze each part of our experimental setup and match measured signal strengths with calculations based on diffusion theory. This agreement between experiment and theory allows us to assert the deep tissue imaging potential of ∼5 cm for UOT of real human tissues predicted by previous theoretical studies [Biomed. Opt. Express 8, 4523 (2017)] with greater confidence, and indicate that future theoretical analysis of optimized UOT systems can be expected to be reliable

Gate-tunable Topological Valley Transport in Bilayer Graphene

Valley pseudospin, the quantum degree of freedom characterizing the degenerate valleys in energy bands, is a distinct feature of two-dimensional Dirac materials. Similar to spin, the valley pseudospin is spanned by a time reversal pair of states, though the two valley pseudospin states transform to each other under spatial inversion. The breaking of inversion symmetry induces various valley-contrasted physical properties; for instance, valley-dependent topological transport is of both scientific and technological interests. Bilayer graphene (BLG) is a unique system whose intrinsic inversion symmetry can be controllably broken by a perpendicular electric field, offering a rare possibility for continuously tunable valley-topological transport. Here, we used a perpendicular gate electric field to break the inversion symmetry in BLG, and a giant nonlocal response was observed as a result of the topological transport of the valley pseudospin. We further showed that the valley transport is fully tunable by external gates, and that the nonlocal signal persists up to room temperature and over long distances. These observations challenge contemporary understanding of topological transport in a gapped system, and the robust topological transport may lead to future valleytronic applications

MEI Kodierung der frühesten Notation in linienlosen Neumen

Das Optical Neume Recognition Project (ONRP) hat die digitale Kodierung von musikalischen Notationszeichen aus dem Jahr um 1000 zum Ziel – ein ambitioniertes Vorhaben, das die Projektmitglieder veranlasste, verschiedenste methodische Ansätze zu evaluieren. Die Optical Music Recognition-Software soll eine linienlose Notation aus einem der ältesten erhaltenen Quellen mit Notationszeichen, dem Antiphonar Hartker aus der Benediktinerabtei St. Gallen (Schweiz), welches heute in zwei Bänden in der Stiftsbibliothek in St. Gallen aufbewahrt wird, erfassen. Aufgrund der handgeschriebenen, linienlosen Notation stellt dieser Gregorianische Gesang den Forscher vor viele Herausforderungen. Das Werk umfasst über 300 verschiedene Neumenzeichen und ihre Notation, die mit Hilfe der Music Encoding Initiative (MEI) erfasst und beschrieben werden sollen. Der folgende Artikel beschreibt den Prozess der Adaptierung, um die MEI auf die Notation von Neumen ohne Notenlinien anzuwenden. Beschrieben werden Eigenschaften der Neumennotation, um zu verdeutlichen, wo die Herausforderungen dieser Arbeit liegen sowie die Funktionsweise des Classifiers, einer Art digitalen Neumenwörterbuchs

Types of Maintenance Based on Uncertain Data Envelope Analysis

Nowadays, one of the main challenges of marine maintenance is how to select an optimum maintenance strategy for each component of the complex ship machinery system. The uncertainty of the parameters is one of the main difficulties encountered. For example, engineers and experts are always questioning the credibility and integrity of the data collected, and historical maintenance records may also be lost during maintenance. The above data asymmetry will also lead to parameter uncertainty, which directly affects the accuracy of the prediction results. This paper proposes a method for determining maintenance types of ship equipment based on uncertainty theory and the Data Envelopment Analysis (DEA) model. Firstly, this paper constructs an uncertain maintenance optimization model (UMOM) based on the classic Data Envelopment Analysis model. Then, we converted the UMOM into an equivalent deterministic model for easy calculation by the uncertainty theory. Finally, a case study is given to verify this model. The results will conclude that the UMOM can meet the need for a reasonable classification of maintenance types of mechanical equipment in a marine system and provide valuable information for systemic management and storage

Consumers’ Willingness to Pay for the Solar Photovoltaic System in the Post-Subsidy Era: A Comparative Analysis under an Urban-Rural Divide

Concerns about the environment and renewable energy are growing. Improving the perception of renewable energy in urban and rural households is required to promote green development and to learn about consumer preferences for renewable energy based on the gradual reduction in financial subsidies for photovoltaic (PV) power generation. This paper aims to estimate the willingness of consumers to pay for a Household PV system and explores the factors that affect consumers’ product selection, which is conducive to optimizing Household PV products and policies and is important for achieving the carbon peaking and carbon neutrality goals. Using a discrete choice model, this paper surveyed 765 urban and rural residents without installing Household PV systems in Tianjin, China. Subsequently, the respondents’ attribute preferences and willingness to pay (WTP) for a Household PV system were analyzed using a logit regression analysis model. The influence of respondents’ socio-economic characteristics on WTP was analyzed. The empirical results showed that (1) price significantly impacts consumers’ PV adoption behaviors and consumers tend to choose cheaper PV products; (2) consumers are more willing to pay for the after-sales service (3959 USD/level) and traceable information (2176 USD/level), indicating their preference for these two attributes when considering options; (3) socio-economic variables, including gender and the number of minor children (i.e., children under the age of 18) at home, significantly impact consumers’ PV adoption behaviors. Males and consumers without minor children at home will pay more attention when selecting the products. Our research findings will provide valuable insights into policy making and the wide-ranging use of Household PV systems

An anomaly detection method based on random convolutional kernel and isolation forest for equipment state monitoring

Anomaly detection plays an essential role in health monitoring and reliability assurance of complex system. However, previous researches suffer from distraction by outliers in training and extensively relying on empiric-based feature engineering, leading to many limitations in the practical application of detection methods. In this paper, we propose an unsupervised anomaly detection method that combines random convolution kernels with isolation forest to tackle the above problems in equipment state monitoring. The random convolution kernels are applied to generate cross-dimensional and multi-scale features for multi-dimensional time series, with combining the time series decomposing method to select abnormally sensitive features for automatic feature extraction. Then, anomaly detection is performed on the obtained features using isolation forests with low requirements for purity of training sample. The verification and comparison on different types of datasets show the performance of the proposed method surpass the traditional methods in accuracy and applicability

UltrasonicGS: A Highly Robust Gesture and Sign Language Recognition Method Based on Ultrasonic Signals

With the global spread of the novel coronavirus, avoiding human-to-human contact has become an effective way to cut off the spread of the virus. Therefore, contactless gesture recognition becomes an effective means to reduce the risk of contact infection in outbreak prevention and control. However, the recognition of everyday behavioral sign language of a certain population of deaf people presents a challenge to sensing technology. Ubiquitous acoustics offer new ideas on how to perceive everyday behavior. The advantages of a low sampling rate, slow propagation speed, and easy access to the equipment have led to the widespread use of acoustic signal-based gesture recognition sensing technology. Therefore, this paper proposed a contactless gesture and sign language behavior sensing method based on ultrasonic signals—UltrasonicGS. The method used Generative Adversarial Network (GAN)-based data augmentation techniques to expand the dataset without human intervention and improve the performance of the behavior recognition model. In addition, to solve the problem of inconsistent length and difficult alignment of input and output sequences of continuous gestures and sign language gestures, we added the Connectionist Temporal Classification (CTC) algorithm after the CRNN network. Additionally, the architecture can achieve better recognition of sign language behaviors of certain people, filling the gap of acoustic-based perception of Chinese sign language. We have conducted extensive experiments and evaluations of UltrasonicGS in a variety of real scenarios. The experimental results showed that UltrasonicGS achieved a combined recognition rate of 98.8% for 15 single gestures and an average correct recognition rate of 92.4% and 86.3% for six sets of continuous gestures and sign language gestures, respectively. As a result, our proposed method provided a low-cost and highly robust solution for avoiding human-to-human contact