Compact Fluorescence Spectrometer with Built-In In-Line Calibration: Application to Detect Dissolved Organic Matter in Water.

Fluorescence spectrometer (FS) is widely used for component analysis because each fluorescing material has its own characteristic spectrum. However, the spectral calibration is complicated and bulky. Herein, an in-line spectral calibration sheet (ISCS) was proposed in which a narrow band-pass filter and a linear variable filter (LVF) were integrated on a metal plate.

Multiple Bioimaging Applications Based on the Excellent Properties of Nanodiamond: A Review.

Nanodiamonds (NDs) are emerging as a promising candidate for multimodal bioimaging on account of their optical and spectroscopic properties. NDs are extensively utilized for bioimaging probes due to their defects and admixtures in their crystal lattice. There are many optically active defects presented in NDs called color centers, which are highly photostable, extremely sensitive to bioimaging, and capable of electron leap in the forbidden band; further, they absorb or emit light when leaping, enabling the nanodiamond to fluoresce.

A Review on the Progress of Optoelectronic Devices Based on TiO Thin Films and Nanomaterials.

Titanium dioxide (TiO) is a kind of wide-bandgap semiconductor. Nano-TiO devices exhibit size-dependent and novel photoelectric performance due to their quantum limiting effect, high absorption coefficient, high surface-volume ratio, adjustable band gap, etc. Due to their excellent electronic performance, abundant presence, and high cost performance, they are widely used in various application fields such as memory, sensors, and photodiodes.

A Review on Optoelectronical Properties of Non-Metal Oxide/Diamond-Based p-n Heterojunction.

Diamond holds promise for optoelectronic devices working in high-frequency, high-power and high-temperature environments, for example in some aspect of nuclear energetics industry processing and aerospace due to its wide bandgap (5.5 eV), ultimate thermal conductivity, high-pressure resistance, high radio frequency and high chemical stability. In the last several years, p-type B-doped diamond (BDD) has been fabricated to heterojunctions with all kinds of non-metal oxide (AlN, GaN, Si and carbon-based semiconductors) to form heterojunctions, which may be widely utilized in various optoelectronic device technology.

Photoluminescence and Electrical Properties of n-Ce-Doped ZnO Nanoleaf/p-Diamond Heterojunction.

The n-type Ce:ZnO (NL) grown using a hydrothermal method was deposited on a p-type boron-doped nanoleaf diamond (BDD) film to fabricate an n-Ce:ZnO NL/p-BDD heterojunction. It shows a significant enhancement in photoluminescence (PL) intensity and a more pronounced blue shift of the UV emission peak (from 385 nm to 365 nm) compared with the undoped heterojunction (n-ZnO/p-BDD). The prepared heterojunction devices demonstrate good thermal stability and excellent rectification characteristics at different temperatures.

Enhanced Photoluminescence and Electrical Properties of n-Al-Doped ZnO Nanorods/p-B-Doped Diamond Heterojunction.

The hydrothermal approach has been used to fabricate a heterojunction of n-aluminum-doped ZnO nanorods/p-B-doped diamond (n-Al:ZnO NRs/p-BDD). It exhibits a significant increase in photoluminescence (PL) intensity and a blue shift of the UV emission peak when compared to the n-ZnO NRs/p-BDD heterojunction. The current voltage () characteristics exhibit excellent rectifying behavior with a high rectification ratio of 838 at 5 V.

Detection of Oil Spill Using SAR Imagery Based on AlexNet Model.

Synthetic aperture radar (SAR) plays an irreplaceable role in the monitoring of marine oil spills. However, due to the limitation of its imaging characteristics, it is difficult to use traditional image processing methods to effectively extract oil spill information from SAR images with coherent speckle noise. In this paper, the convolutional neural network AlexNet model is used to extract the oil spill information from SAR images by taking advantage of its features of local connection, weight sharing, and learning for image representation.

Hybrid Regularization of Diffusion Process for Visual Re-Ranking.

To improve the retrieval result obtained from a pairwise dissimilarity, many variants of diffusion process have been applied in visual re-ranking. In the framework of diffusion process, various contextual similarities can be obtained by solving an optimization problem, and the objective function consists of a smoothness constraint and a fitting constraint. And many improvements on the smoothness constraint have been made to reveal the underlying manifold structure.

Identification of adhesion-associated DNA methylation patterns in the peripheral nervous system.

Schwann cells are unique glial cells in the peripheral nervous system. These cells provide a range of cytokines and nutritional factors to maintain axons and support axonal regeneration. However, little is known concerning adhesion-associated epigenetic changes that occur in Schwann cells after peripheral nerve injury (PNI).

A Collective Neurodynamic Optimization Approach to Nonnegative Matrix Factorization.

Nonnegative matrix factorization (NMF) is an advanced method for nonnegative feature extraction, with widespread applications. However, the NMF solution often entails to solve a global optimization problem with a nonconvex objective function and nonnegativity constraints. This paper presents a collective neurodynamic optimization (CNO) approach to this challenging problem.

Trace analysis of oil-in-water by using visible LED and metal waveguide capillary.

Trace analysis of oil-in-water (O/W) has wide applications in life science, industry and environmental monitoring (such as oil spilling). In this paper, with the aid of surfactant, diesel was dispersed in water as O/W emulsion, which can be detected by using visible LED and metal-waveguide-capillary (MWC). Due to the enhancement of optical-path and related light-droplet interaction in MWC, detecting diesel of a concentration as low as 2.

A Collective Neurodynamic Approach to Constrained Global Optimization.

Global optimization is a long-lasting research topic in the field of optimization, posting many challenging theoretic and computational issues. This paper presents a novel collective neurodynamic method for solving constrained global optimization problems. At first, a one-layer recurrent neural network (RNN) is presented for searching the Karush-Kuhn-Tucker points of the optimization problem under study.

A dynamic feedforward neural network based on gaussian particle swarm optimization and its application for predictive control.

A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN.