Distributed Fiber Ultrasonic Sensor and Pattern Recognition Analytics

Ultrasound interrogation and structural health monitoring technologies have found a wide array of applications in the health care, aerospace, automobile, and energy sectors. To achieve high spatial resolution, large array electrical transducers have been used in these applications to harness sufficient data for both monitoring and diagnoses. Electronic-based sensors have been the standard technology for ultrasonic detection, which are often expensive and cumbersome for use in large scale deployments. Fiber optical sensors have advantageous characteristics of smaller cross-sectional area, humidity-resistance, immunity to electromagnetic interference, as well as compatibility with telemetry and telecommunications applications, which make them attractive alternatives for use as ultrasonic sensors. A unique trait of fiber sensors is its ability to perform distributed acoustic measurements to achieve high spatial resolution detection using a single fiber. Using ultrafast laser direct-writing techniques, nano-reflectors can be induced inside fiber cores to drastically improve the signal-to-noise ratio of distributed fiber sensors. This dissertation explores the applications of laser-fabricated nano-reflectors in optical fiber cores for both multi-point intrinsic Fabry–Perot (FP) interferometer sensors and a distributed phase-sensitive optical time-domain reflectometry (φ-OTDR) to be used in ultrasound detection. Multi-point intrinsic FP interferometer was based on swept-frequency interferometry with optoelectronic phase-locked loop that interrogated cascaded FP cavities to obtain ultrasound patterns. The ultrasound was demodulated through reassigned short time Fourier transform incorporating with maximum-energy ridges tracking. With tens of centimeters cavity length, this approach achieved 20kHz ultrasound detection that was finesse-insensitive, noise-free, high-sensitivity and multiplex-scalability. The use of φ-OTDR with enhanced Rayleigh backscattering compensated the deficiencies of low inherent signal-to-noise ratio (SNR). The dynamic strain between two adjacent nano-reflectors was extracted by using 3×3 coupler demodulation within Michelson interferometer. With an improvement of over 35 dB SNR, this was adequate for the recognition of the subtle differences in signals, such as footstep of human locomotion and abnormal acoustic echoes from pipeline corrosion. With the help of artificial intelligence in pattern recognition, high accuracy of events’ identification can be achieved in perimeter security and structural health monitoring, with further potential that can be harnessed using unsurprised learning

Unsupervised Deep Hashing for Large-scale Visual Search

Learning based hashing plays a pivotal role in large-scale visual search. However, most existing hashing algorithms tend to learn shallow models that do not seek representative binary codes. In this paper, we propose a novel hashing approach based on unsupervised deep learning to hierarchically transform features into hash codes. Within the heterogeneous deep hashing framework, the autoencoder layers with specific constraints are considered to model the nonlinear mapping between features and binary codes. Then, a Restricted Boltzmann Machine (RBM) layer with constraints is utilized to reduce the dimension in the hamming space. Extensive experiments on the problem of visual search demonstrate the competitiveness of our proposed approach compared to state-of-the-art

Active compounds: A new direction for rice value addition

The development of rice active compounds is conducive to improving the added value of rice. This paper focused on the types and effects of active compounds in rice. Furthermore, it summarized the effect of rice storage and processing technology on rice active compounds. We conclude the following: Rice contains a large number of active compounds that are beneficial to humans. At present, the research on the action mechanism of rice active compounds on the human body is not deep enough, and the ability to deeply process rice is insufficient, greatly limiting the development of the rice active compound industry. To maximize the added value of rice, it is necessary to establish a dedicated preservation and processing technology system based on the physicochemical properties of the required active compounds. Additionally, attention should be paid to the development and application of composite technologies during the development of the rice active compound industry

Unsupervised deep hashing for large-scale visual search

Abstract Learning based hashing plays a pivotal role in large-scale visual search. However, most existing hashing algorithms tend to learn shallow models that do not seek representative binary codes. In this paper, we propose a novel hashing approach based on unsupervised deep learning to hierarchically transform features into hash codes. Within the heterogeneous deep hashing framework, the autoencoder layers with specific constraints are considered to model the nonlinear mapping between features and binary codes. Then, a Restricted Boltzmann Machine (RBM) layer with constraints is utilized to reduce the dimension in the hamming space. The experiments on the problem of visual search demonstrate the competitiveness of our proposed approach compared to the state of the art

Design, synthesis and anti-HIV evaluation of novel diarylnicotinamide derivatives (DANAs) targeting the entrance channel of the NNRTI binding pocket through structure-guided molecular hybridization

Through a structure-based molecular hybridization approach, a novel series of diarylnicotinamide derivatives (DANAs) targeting the entrance channel of HIV-1 NNRTIs binding pocket (NNIBP) were rationally designed, synthesized and evaluated for their anti-HIV activities in MT-4 cells together with the inhibition against the reverse transcriptase (RT) in an enzymatic assay. Encouragingly, most of the new DANAs were found to be active against wild-type HIV-1 with an EC50 in the range of 0.027-4.54 μM. Among them, compound 6b11 (EC50 = 0.027 μM, SI > 12518) and 6b5 (EC50 = 0.029 μM, SI = 2471) were identified as the most potent inhibitors, which were more potent than the reference drugs nevirapine (EC50 = 0.31 μM) and delavirdine (EC50 = 0.66 μM). Some DANAs were also active at micromolar concentrations against the K103N + Y181C resistant mutant. Compound 6b11 exhibited the highest enzymatic inhibition activity (IC50 = 20 nM), which is equal to that of efavirenz (EC50 = 20 nM) and 31 times higher than that of nevirapine (EC50 = 0.62 μM). Preliminary structure-activity relationships (SARs) and molecular modeling of these new DANAs have been discussed.publisher: Elsevier articletitle: Design, synthesis and anti-HIV evaluation of novel diarylnicotinamide derivatives (DANAs) targeting the entrance channel of the NNRTI binding pocket through structure-guided molecular hybridization journaltitle: European Journal of Medicinal Chemistry articlelink: http://dx.doi.org/10.1016/j.ejmech.2014.09.054 content_type: article copyright: Copyright © 2014 Elsevier Masson SAS. All rights reserved.status: publishe

Revealing the invisible with model and data shrinking for composite-database micro-expression recognition

Abstract Composite-database micro-expression recognition is attracting increasing attention as it is more practical for real-world applications. Though the composite database provides more sample diversity for learning good representation models, the important subtle dynamics are prone to disappearing in the domain shift such that the models greatly degrade their performance, especially for deep models. In this article, we analyze the influence of learning complexity, including input complexity and model complexity, and discover that the lower-resolution input data and shallower-architecture model are helpful to ease the degradation of deep models in composite-database task. Based on this, we propose a recurrent convolutional network (RCN) to explore the shallower-architecture and lower-resolution input data, shrinking model and input complexities simultaneously. Furthermore, we develop three parameter-free modules (i.e., wide expansion, shortcut connection and attention unit) to integrate with RCN without increasing any learnable parameters. These three modules can enhance the representation ability in various perspectives while preserving not-very-deep architecture for lower-resolution data. Besides, three modules can further be combined by an automatic strategy (a neural architecture search strategy) and the searched architecture becomes more robust. Extensive experiments on the MEGC2019 dataset (composited of existing SMIC, CASME II and SAMM datasets) have verified the influence of learning complexity and shown that RCNs with three modules and the searched combination outperform the state-of-the-art approaches

Spatio-temporal pain estimation network with measuring pseudo heart rate gain

Abstract Pain is a significant indicator that shows people are suffering from an unwell experience and its automatic estimation has attracted much interest in recent years. Of late, most estimation methods are designed to capture the dynamic pain information from visual signals while a few physiological-signal based methods can provide extra potential cues to analyze the pain more accurately. However, it is still challenging to capture the physiological data from patients as it requires contact devices and patients’ cooperation. In this paper, we propose to leverage the pseudo physiological information by generating new modal data from the original visual videos and jointly estimating the pain by an end-to-end network. To extract the representations from bi-modal data, we design a spatio-temporal pain estimation network, which employs a dual-branch framework for extracting pain-aware visual and pseudo physiological features separately and fuses the features in a probabilistic way. The inherent vital sign, i.e., heart rate gain (HRG), from pseudo physiological information can be utilized as an auxiliary signal and integrated with the visual pain estimation framework. Moreover, specially-designed 3D convolution filters and attention structures are employed to extract spatio-temporal features for both branches. To use the HRG as an auxiliary way for pain estimation, we propose a probabilistic inference model by jointly considering the visual branch and physiological branch, which makes our model estimate the pain comprehensively. Experiments on two publicly-available datasets show the effectiveness of introducing the pseudo modality, and the proposed method can outperform the state-of-the-art methods