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This paper aims to develop a general framework for accurately tracking and quantitatively characterizing multiple cells (objects) when collision and division between cells arise. Through introducing three types of interaction events among cells, namely, independence, collision, and division, the corresponding dynamic models are defined and an augmented interacting multiple model particle filter tracking algorithm is first proposed for spatially adjacent cells with varying size. In addition, to reduce the ambiguity of correspondence between frames, both the estimated cell dynamic parameters and cell size are further utilized to identify cells of interest. The experiments have been conducted on two real cell image sequences characterized with cells collision, division, or number variation, and the resulting dynamic parameters such as instant velocity, turn rate were obtained and analyzed.

Traditional blood group interpretation technology has poor detection efficiency and interpretation accuracy in the face of complex conditions in clinical environments. In order to improve the interpretation accuracy of the automatic blood group interpretation system, the important role of deep learning in the blood group interpretation system was studied. Based on the AlexNet network model, this paper proposes an improved scheme because of its advantages in terms of speeding up the convergence training speed and enhancing the model's generalizability. However, it still needs improvement in terms of blood group interpretation accuracy. The improved AlexNet network model proposed in this paper added an attention mechanism to the network structure, optimized the loss function in the training algorithm, and adjusted the learning rate attenuation function. The experiments showed that compared with the accuracy of the AlexNet model, its training effect was remarkable, with an accuracy of 96.9%—an increase of 3%. Moreover, the improved network model paid more attention to fine-grained classification, minimized the loss rate, and improved the accuracy of system interpretation. [ABSTRACT FROM AUTHOR]

This paper focuses on presenting a new identification algorithm to estimate the parameters and state variables for two‐input two‐output dynamic systems with time delay based on canonical state space models. First, the related input‐output equation is determined and transformed into an identification oriented model, which does not involve in the unmeasurable states, and then a residual based least squares identification algorithm is presented for the estimations. After the parameters being estimated, the system states are subsequently estimated by using the estimated parameters. Through theoretical analysis, the convergence of the algorithm is derived to provide assurance for applicability. Finally, a selected simulation example is given for a meaningful case study to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a multi-innovation stochastic gradient parameter estimation algorithm for dual-rate sampled state-space systems with d -step time delay by the multi-innovation identification theory. Considering the stochastic disturbance in industrial process and using the gradient search, a multi-innovation stochastic gradient algorithm is proposed through expanding the scalar innovation into an innovation vector in order to obtain more accurate parameter estimates. The difficulty of identification is that the information vector in the identification model contains the unknown states. The proposed algorithm uses the state estimates of the observer instead of the state variables to realize the parameter estimation. The simulation results indicate that the proposed algorithm works well. [ABSTRACT FROM AUTHOR]

In terms of the varying number of cell population, shape deformation, collision and uneven movement, a novel method based on multi-task particle swarm optimization (PSO) algorithm without explicit detection module, named MTPSO tracking method, is developed for automatic tracking of biological cells in time-lapse low-contrast microscopy image sequences. For tracking existing cells from the previous frames, a PSO-based tracking module is firstly implemented to give the initial positions of existing cells according to the previous estimated state of each cell, then a PSO-based contour module is proposed to determine the corresponding contour of each cell and finally achieve a precise position tracking by an iterative centroid updating process. For tracking new appearing cells at the current frame, a PSO-based discovery module, followed by the aforementioned PSO-based contour module, is proposed to search for new potential cells through appropriate initialization of particle swarm and searching mechanism. MTPSO tracking method is tested over a number of different real cell image sequences and is shown to provide high accuracy both in position and contour estimate of each cell in various challenging cases. Furthermore, it is more competitive against the state-of-the-art multi-object tracking methods in terms of performance measures such as FAR, FNR, LTR, and LSR. [ABSTRACT FROM AUTHOR]

Inspired by ant's stochastic behavior in search for multiple food sources, we propose a cooperating multi-task ant system for tracking multiple synthetic objects as well as multiple real cells in a bio-medical field. In our framework, each ant colony is assumed and assigned to fulfill a given task to estimate the state of an object. Furthermore, two ant levels are used, i.e., ant individual level and ant cooperation level. In the ant individual level, ants within one colony perform independently, and the motion of each individual is probabilistically determined by both its intended motion modes and the likelihood function score. In the ant cooperation level, each ant adjusts individual state within its influence region according to heuristic information of all other ants within the same colony, while the global best template at current iteration is found among all ant colonies and utilized to update ant model probability, influence region, and probability of fulfilling task. Our algorithm is validated by comparing it to the-state-of-art algorithms, and specifically the improved tracking performance in terms of false negative rate (up to 10.0%) and false negative rate (up to 2.1%) is achieved based on the studied three real cell image sequences. [ABSTRACT FROM AUTHOR]