Abstract:Froth image segmentation is an important and basic part in an online froth monitoring system in mineral processing. The fast and accurate bubble delineation in a froth image is significant for the subsequent froth surface characterization. This paper proposes a froth image segmentation method combining image classification and image segmentation. In the method, an improved Harris corner detection algorithm is applied to classify froth images first. Then, for each class, the images are segmented by automatically choosing the corresponding parameters for identifying bubble edge points through extracting the local gray value minima. Finally, on the basis of the edge points, the bubbles are delineated by using a number of post-processing functions. Compared with the widely used Watershed algorithm and others for a number of lead zinc froth images in a flotation plant, the new method (algorithm) can alleviate the over-segmentation problem effectively. The experimental results show that the new method can produce good bubble delineation results automatically. In addition, its processing speed can also meet the online measurement requirements.
With the development of wireless communication technology, indoor tracking technology has been rapidly developed. Wits presents a new indoor positioning and tracking algorithm with channel state information of Wi-Fi signals. Wits tracks using motion speed. Firstly, it eliminates static path interference and calibrates the phase information. Then, the maximum likelihood of the phase is used to estimate the radial Doppler velocity of the target. Experiments were conducted, and two sets of receiving antennas were used to determine the velocity of a human. Finally, speed and time intervals were used to track the target. Experimental results show that Wits can achieve the mean error of 0.235 m in two different environments with a known starting point. If the starting point is unknown, the mean error is 0.410 m. Wits has good accuracy and efficiency for practical applications.
Indoor human wireless sensing is crucial for many applications such as smart homes and security monitoring. It needs to understand both the number of people and their activities, which is often difficult, especially in large spaces. This paper proposes a combined people number counting and action recognition method by using the channel state information (CSI) of Wi-Fi signal, which aims to simultaneously estimate the number of people and their actions in wireless sensing applications. First, a jump region removal algorithm is developed to calibrate the phase difference. Second, a cumulative sliding variance algorithm is designed for the detection of moving targets in the environment. Then a multidimensional feature is formed by the amplitude, frequency domain, and phase difference of CSI, and based on it, a two-level classifier based on the SVM algorithm is used to estimate the number of people and their actions. Experimental results show that the average accuracy of the system for the people counting is 93.5%, and the activity recognition rate of one-person and two-person is 99.6% and 94.6% respectively.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Counting the number of people and estimating their walking speeds are essential in crowd control and flow. In this work, we propose a system that uses prevalent Wi-Fi signals to identify the number of people entering and leaving a room through a door. It selects the best subcarrier of Wi-Fi signals and applies the Hampel filter to remove outlier information first. Then, it employs a double threshold method to determine the start and end times of entering or leaving. Afterward, it compares the detected signals with the precollected database using the dynamic time-warping algorithm and determines the number of people. It uses a variance threshold method to identify the states of entering or leaving. It also employs a nonlinear fitting approach to calculate the walking speeds. The experiments show that, in a large empty laboratory, the accuracy rates in determining the number of people are 100% for one person, 81% for two persons, and 95% for three persons. In a small office, the accuracy rates for detecting the number of people are 98% for one or two persons, 82% for three persons, 93% for four, and 75% for five persons. For the walking speed estimation, the accuracy rate for a speed error of less than 0.2410 m/s is 75% for a single person.
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