This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.

Automatic learning feedback monitoring and analysis are becoming essential in modern education. We present a video analytic system capable of monitoring in-class student’s learning behaviors and providing feedback to the instructor. It is a common practice nowadays for students to take electronic notes or browse online using laptops and cellphones in class. However the use of technology can also impact student concentration and affect learning behaviors, which can seriously hinder their learning progress if not controlled properly. In this pioneering study, we propose a non-intrusive deep-learning based computer vision system to monitor student concentration by extracting and inferring high-level visual behavior cues, including their facial expressions, gestures and activities. Our system can automatically assist instructors with situational awareness in real time. We assume only RGB color images as input and runable system on edge devices for easy deployment. We propose two video analytic components for student behavior analysis: (1) The facial analysis component operates based on Dlib face detection and facial landmark tracking to localize each student and analyze their face orientations, eye blinking, gazes, and facial expressions. (2) The activity detection and recognition component operates based on OpenPose and COCO object detection can identify eight types of in-class gestures and behaviors including raising-hand, typing, phone-answering, crooked-head, desk napping, etc. Experiments are performed on a newly collected real-world In-Class Student Activity Dataset (ICSAD), where we achieved nearly 80% activity detection rate. Our system is view-independent in handling facial and pose orientations with average angular error < 10°. The source code of this work is at: https://github.com/YiZengHsieh/ICSAD .

Popular methods to deal with computation become strenuous due to the optimization demands that develop more complex nowadays. This research aims to propose a new optimal algorithm, Dove Swarm Optimization (DSO), that adopts the foraging behaviors of doves to have six benchmark functions expressing DSO performance. By considering competition for forage, DSO is designed to ensure the most satisfied dove as well as optimization, then compared with 15 popular optimization algorithms using random initial and lattice initial values. The results reveal that DSO performs the best in time efficiency and well in both convergences for these functions in a reasonable region from 1 to 3 seconds, and population diversity for the initialization method from less than 1 second to 9 seconds dependent on the population size. As a result, DSO is indeed a time-efficient and effective algorithm in solving optimization problems.