An online monitoring device for measuring the concentration of four types of in-situ microorganisms by using the near-infrared band.

Using optical density at 600 nm (OD600) to measure the microbial concentration is a popular approach due to its advantages like quick response and non-destructive. However, the OD600 measurement might be affected by the metabolic pigment, and it would become invalid when the solution dilution is insufficient. To overcome these issues, we proposed to adopt a more robust wavelength at 890 nm to quantify the attenuation of transmission light. After selecting this light source, we designed the light path and the circuit of the online monitoring device. Meanwhile, the random forest algorithm was introduced for temperature compensation and improving the stability of the device. This device was verified by monitoring the microbial concentration of four strains (Yeast, Bacillus, Arthrobacter, and Escherichia coli). The experimental result suggested that the mean absolute percentage error reached 4.11 %, 4.28 %, 4.49 %, and 4.53 % respectively, which is helpful to improve the accuracy of microbial concentration measurement.

Dynamically Spatiotemporal Regularized Correlation Tracking.

Recently, due to the high performance, spatially regularized strategy has been widely applied to addressing the issue of boundary effects existed in correlation filter (CF)-based visual tracking. Specifically, it introduces a spatially regularized term to penalize the coefficients of the CFs to be learned depending on their spatial locations. However, the regularization weights are often formed as a fixed Gaussian function, and hence may cause the learned model degenerate due to the inflexible constraints on the ever-changing CFs to be learned over time during tracking. To address this issue, in this paper, we develop a dynamically spatiotemporal regularization model to constrain the CFs to be learned with the ever-changing regularization weights learned from two consecutive frames. The proposed method jointly learns the CFs along with the dynamically spatiotemporal constraint term, which can be efficiently solved in the Fourier domain by the alternative direction method. Extensive evaluations on the popular data sets OTB-100 and VOT-2016 demonstrate that the proposed tracker performs favorably against the baseline tracker and several recently proposed state-of-the-art methods.

Parallel Attentive Correlation Tracking.

Psychological and cognitive findings indicate that human visual perception is attentive and selective, which may process spatial and appearance selective attentions in parallel. By reflecting some aspects of these attentions, this paper presents a novel correlation filter (CF) based tracking approach, corresponding to processing a local and a semi-local background domains, respectively. In the local domain, inspired by the Gestalt principle of figure-ground segregation, we leverage an efficient Boolean map representation, which characterizes an image by a set of Boolean maps via randomly thresholding its color channels, yielding a location response map as a weighted sum of all Boolean maps. The Boolean maps capture the topological structures of target and its scene with different granularities, thereby enabling to effectively improve tracking of non-rectangular objects. Alternatively, in the semi-local domains, we introduce a novel distractor-resilient metric regularization into CF, which acts as a force to push distractors into negative space. Consequently, the unwanted boundary effects of CF can be effectively alleviated. Finally, both models associated with the local and the semi-local domains are seamlessly integrated into a Bayesian framework, and the tracked location is determined by maximizing its likelihood function. Extensive evaluations on the OTB50, OTB100, VOT2016 and VOT2017 tracking benchmarks demonstrate that the proposed method achieves favorable performance against a variety of state-of-the-art trackers with a speed of 45 fps on a single CPU.