Deep Learning based Cell Classification in Imaging Flow Cytometer

Deep learning is an idea technique for image classification. Imaging flow cytometer enables high throughput cell image acquisition and some have integrated with real-time cell sorting. The combination of deep learning and imaging flow cytometer has changed the landscape of high throughput cell analysis research. In this review, we focus on deep learning technologies applied in imaging flow cytometer for cell classification and real-time cell sorting. This article describes some recent research, challenges and future trend in this area.

A New Location Sensing Algorithm Based on DV-Hop and Quantum-Behaved Particle Swarm Optimization in WSN

In wireless sensor network, the location sensing of the sensor nodes is practical. If there is no location information of the sensor nodes, the perceived data would have no meaning. In recent years, the range-free location sensing algorithms have got great attention. DV-Hop localization algorithm is one of the important algorithm in range-free location algorithms. It has high efficiency, convenient operation and low energy consumption. However, the localization accuracy cannot meet the requirements in some applications. In this paper, a new localization method is proposed, which is based on DV-Hop and Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, it deals with the high influence of average single jumping distance and then modifies the calculation of it in the DV-Hop algorithm. Second, in order to solve the problem of the coordinate optimization in the DV-Hop algorithm, this study chooses QPSO algorithm to optimize the unknown nodes’ coordinates. Simulation results show that the new method can improve the localization accuracy of the unknown nodes obviously in WSN.

Finger Vein Image Enhancement Based on Guided Tri-Gaussian Filters

In the process of image acquisition, the contrast between veins and non-veins in finger vein images is not high due to the influence of the fuzzy light source, skin scattering and finger movement. To solve this problem, a finger vein image enhancement method is proposed (GTGFs), which enhances finger vein patterns by setting guided image as input image firstly. On this basis, the tri-Gaussian model is based on disinhibitory properties of the concentric receptive field used to locally enhancing the image. The parameters of the tri-Gaussian model are determined based on the finger vein width information. The experiment results show that the proposed enhancement method can significantly enhance the finger vein patterns and improve the recognition effect of the methods based on vein pattern segmentation.

Polynomial Fitting Algorithm Based on Neural Network

As a method of function approximation, polynomial fitting has always been the main research hotspot in mathematical modeling. In many disciplines such as computer, physics, biology, neural networks have been widely used, and most of the applications have been transformed into fitting problems using neural networks. One of the main reasons that neural networks can be widely used is that it has a certain sense of universal approximation. In order to fit the polynomial, this paper constructs a three-layer feedforward neural network, uses Taylor series as the activation function, and determines the number of hidden layer neurons according to the order of the polynomial and the dimensions of the input variables. For explicit polynomial fitting, this paper uses non-linear functions as the objective function, and compares the fitting effects under different orders of polynomials. For the fitting of implicit polynomial curves, the current popular polynomial fitting algorithms are compared and analyzed. Experiments have proved that the algorithm used in this paper is suitable for both explicit polynomial fitting and implicit polynomial fitting. The algorithm is relatively simple, practical, easy to calculate, and can efficiently achieve the fitting goal. At the same time, the computational complexity is relatively low, which has certain application value.

Self-monitoring Method for Improving Health-related Quality of Life: Data Acquisition, Monitoring, and Analysis of Vital Signs and Diet

Vital signs are a series of clinical measurements and important to health-related quality of life. To establish a method for self-monitoring and management of vital signs and diet, a self-monitoring method (SMM) including wireless body area network and mobile technology was proposed in this paper. The study population comprised a total of 180 participants. Differences between measurements taken using the SMM and traditional instruments were analyzed with respect to accuracy and reproducibility. Participant measurements before and after intervention were used to evaluate the validity of the SMM. There was no statistically significant difference between our SMM and traditional instruments for measuring vital signs (p>0.05). The relative standard deviation of the SMM (0.38%) indicated good repeatability. These findings suggest that the SMM had a clear effect of promoting improvement in health habits, health condition monitoring, and disease prevention (p<0.05). Statistical analysis indicated that our SMM contributed to improve monitoring of vital signs and diet, and improved the health-related quality of life among study participants to a certain degree.