We use an evolutionary mechanism to improve the image segmentation process.We optimize the minimum cross entropy with an evolutionary method for image segmentation.We test the approach in multidimensional spaces.An alternative method for MR brain image segmentation is proposed.Comparisons and non-parametric test support the experimental results. Segmentation is considered the central part of an image processing system due to its high influence on the posterior image analysis. In recent years, the segmentation of magnetic resonance (MR) images has attracted the attention of the scientific community with the objective of assisting the diagnosis in different brain diseases. From several techniques, thresholding represents one of the most popular methods for image segmentation. Currently, an extensive amount of contributions has been proposed in the literature, where thresholding values are obtained by optimizing relevant criteria such as the cross entropy. However, most of such approaches are computationally expensive, since they conduct an exhaustive search strategy for obtaining the optimal thresholding values. This paper presents a general method for image segmentation. To estimate the thresholding values, the proposed approach uses the recently published evolutionary method called the Crow Search Algorithm (CSA) which is based on the behavior in flocks of crows. Different to other optimization techniques used for segmentation proposes, CSA presents a better performance, avoiding critical flaws such as the premature convergence to sub-optimal solutions and the limited exploration-exploitation balance in the search strategy. Although the proposed method can be used as a generic segmentation algorithm, its characteristics allow obtaining excellent results in the automatic segmentation of complex MR images. Under such circumstances, our approach has been evaluated using two sets of benchmark images; the first set is composed of general images commonly used in the image processing literature, while the second set corresponds to MR brain images. Experimental results, statistically validated, demonstrate that the proposed technique obtains better results in terms of quality and consistency.

Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm

The Global Positioning System (GPS) is widely used in outdoor environmental positioning. However, GPS cannot support indoor positioning because there is no signal for positioning in an indoor environment. Nowadays, there are many situations which require indoor positioning, such as searching for a book in a library, looking for luggage in an airport, emergence navigation for fire alarms, robot location, etc. Many technologies, such as ultrasonic, sensors, Bluetooth, WiFi, magnetic field, Radio Frequency Identification (RFID), etc., are used to perform indoor positioning. Compared with other technologies, RFID used in indoor positioning is more cost and energy efficient. The Traditional RFID indoor positioning algorithm LANDMARC utilizes a Received Signal Strength (RSS) indicator to track objects. However, the RSS value is easily affected by environmental noise and other interference. In this paper, our purpose is to reduce the location fluctuation and error caused by multipath and environmental interference in LANDMARC. We propose a novel indoor positioning algorithm based on Bayesian probability and K-Nearest Neighbor (BKNN). The experimental results show that the Gaussian filter can filter some abnormal RSS values. The proposed BKNN algorithm has the smallest location error compared with the Gaussian-based algorithm, LANDMARC and an improved KNN algorithm. The average error in location estimation is about 15 cm using our method.

An RFID Indoor Positioning Algorithm Based on Bayesian Probability and K-Nearest Neighbor.

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice.

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A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing - Part I: Fundamentals and Enabling Technologies

Fifth-generation (5G) networks providing much higher bandwidth and faster data rates will allow connecting vast number of stationary and mobile devices, sensors, agents, users, machines, and vehicles, supporting Internet-of-Things (IoT), real-time dynamic networks of mobile things . Positioning and location awareness will become increasingly important, enabling deployment of new services and contributing to significantly improving the overall performance of the 5G system. Many of the currently talked about solutions to positioning in 5G are centralized, mostly requiring direct communication to the access nodes (or anchors, i.e., nodes with known locations), which in turn requires a high density of anchors. But such centralized positioning solutions may become unwieldy as the number of users and devices continues to grow without limit in sight. As an alternative to the centralized solutions, this paper discusses distributed localization in a 5G-enabled IoT environment where many low power devices, users, or agents are to locate themselves without a direct access to anchors. Even though positioning is essentially a nonlinear problem (solving circle equations by trilateration or triangulation), we discuss a cooperative linear distributed iterative solution with only local measurements, local communication, and local computation needed at each agent. Linearity is obtained by reparametrization of the agent location through barycentric coordinate representations based on local neighborhood geometry that may be computed in terms of certain Cayley–Menger determinants involving relative local inter-agent distance measurements. After a brief introduction to the localization problem, and other available distributed solutions primarily based on directly addressing the nonlinear formulation, we present the distributed linear solution for stationary agent networks and study its convergence, its robustness to noise, and extensions to mobile scenarios, in which agents, users, and (possibly) anchors are dynamic.

Distributed Localization: A Linear Theory

Indoor location identification technologies for real-time IoT-based applications: An inclusive survey

Inertial navigation systems (INS) are composed of inertial sensors, such as accelerometers and gyroscopes. An INS updates its orientation and position automatically; it has an acceptable stability over the short term, however this stability deteriorates over time. Odometry, used to estimate the position of a mobile robot, employs encoders attached to the robot’s wheels. However, errors occur caused by the integrative nature of the rotating speed and the slippage between the wheel and the ground. In this paper, we discuss mobile robot position estimation without using external signals in indoor environments. In order to achieve optimal solutions, a Kalman filter that estimates the orientation and velocity of mobile robots has been designed. The proposed system combines INS and odometry and delivers more accurate position information than standalone odometry.

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A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding

In the indoor environment, variation of the magnetic field is caused by building structures, and magnetic field map navigation is based on this feature. In order to estimate position using this navigation, a three-axis magnetic field must be measured at every point to build a magnetic field map. After the magnetic field map is obtained, the position of the mobile robot can be estimated with a likelihood function whereby the measured magnetic field data and the magnetic field map are used. However, if only magnetic field map navigation is used, the estimated position can have large errors. In order to improve performance, we propose a particle filter system that integrates magnetic field map navigation and an encoder system. In this paper, multiple magnetic sensors and three magnetic field maps (a horizontal intensity map, a vertical intensity map, and a direction information map) are used to update the weights of particles. As a result, the proposed system estimates the position and orientation of a mobile robot more accurately than previous systems. Also, when the number of magnetic sensors increases, this paper shows that system performance improves. Finally, experiment results are shown from the proposed system that was implemented and evaluated.

https://www.mdpi.com/1424-8220/17/3/651/pdf

Indoor Positioning System Using Magnetic Field Map Navigation and an Encoder System.

Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery

Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery.

Aim This study aims to evaluate the extrinsic effects of conditional factors affecting quantitative parameters and to establish the optimization of indocyanine green (ICG) angiography using in vitro experiments and prospective observational study. Method In vitro experiments were performed to evaluate the correlation between conditional factors, such as camera distance, surrounding lighting, fluorescence emission sources, and ICG doses. The fluorescence intensity was measured from the ICG contained test tube in each condition. In the clinical study, ICG angiography was applied to patients with colorectal cancer (n = 164). The quantitative perfusion parameters were included as maximal fluorescence intensity (FMAX ), Slope, T1/2MAX , and perfusion time ratio (TR). Camera position, distance to colon, fluorescence emission source, surrounding lighting, site of angiography, and ICG specific modes were considered as conditional factors and compared with quantitative parameters to identify the optimal condition of ICG angiography. Results The fluorescence intensity had an inverse correlation with distance, and the transitional zone was shown at the distance of 4-5 cm by slope differential. FMAX , T1/2MAX , and Slope were affected significantly by camera distance, site of angiography, fluorescence emission source, ICG modes as conditional factors. On multivariate analysis, FMAX was independently associated with spectral ICG mode with red inversion, Laser mode, and camera distance. Conversely, TR was not related to any conditional factors. Conclusion Since quantitative parameters of ICG angiography are influenced by various conditions, a standardized protocol is required. The application of ICG specific modes with a constant distance of 4-5 cm can provide optimized fluorescence images.

Optimization of indocyanine green angiography for colon perfusion during laparoscopic colorectal surgery.

Odometry is a method that calculates the position and heading angle of a mobile robot using encoders attached to the wheels of the robot. Errors in the position and heading angle in odometry continuously increase as the operating time and moving distance increase. The solution to overcome these accumulated errors is to periodically compensate with the external absolute position information. An ultrasonic local positioning system (LPS) consists of multiple ultrasonic transmitters located in the environment and an ultrasonic receiver. In this study, ultrasonic transmitters are in a line at one side, and four transmitters are grouped for a coverage area. In order to measure the time of flights (TOFs) for an ultrasonic signal, the receiver predicted the transmitted time from each transmitter using a hyperbolic model. Four transmitters emit ultrasonic signals sequentially, and then the receiver calculates the position using the present measured distance and the pre-measured distance. In order to extend the distance that is measured, the receiver collects the ultrasonic signal and executes cross correlation with a sinusoidal signal. The measured distance data of the previous step causes the position error. This error is compensated for by the predicted distance data using a bilinear interpolation method. An extended Kalman filter is designed to combine odometry, a compass sensor, and an ultrasonic LPS. The proposed system provides reliable and accurate position and heading information, regardless of the operating time and moving distance.

Kwang-Ryul Baek

Papers

A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding

Indoor Positioning System Using Magnetic Field Map Navigation and an Encoder System.

Artificial intelligence based real-time microcirculation analysis system for laparoscopic colorectal surgery.

Optimization of indocyanine green angiography for colon perfusion during laparoscopic colorectal surgery.

Positioning of a mobile robot based on odometry and a new ultrasonic LPS