A large number of web-based e-Health applications have been developed through time, allowing doctors to access different types of functionalities, like knowing which medication the patient has consumed or performing online consultations. Internet systems for healthcare can be improved by using artificial intelligence mechanisms for the process of detecting diseases and obtaining biological data, allowing medical professionals to have important information that facilitates the diagnosis process and the choice of the correct treatment for each particular person.The proposed research work aims to present an innovative approach when compared to traditional platforms, by providing online vital signs in real time and allowing the visualization of all historical data of a patient. It aims to defend the concept of promoting online consultations, providing complementary functionalities to the traditional methods for performing medical diagnoses through the use of software engineering practices. This investigation led to the conclusion that, in the future, many medical processes will most likely be done online, where this practice is considered extremely helpful for the analysis and treatment of contagious diseases, or cases that require constant monitoring.
Being able to identify fish in aquaculture is essential to optimize their feeding and thereby reduce the cost of production. The use of radio frequency has proven to be a powerful and effective technology for the identification of fish in aquaculture cages. A passive tag inserted into the fish sent information wirelessly from the tag to a receiver at some distance away. The integration of RFID with Internet of Thing (IoT) is a powerful tool not only for the identification of fish but also for fish control to ensure food safety. In this work, we proposed an affordable IoT architecture to increase the access to the data from any device. A prototype was developed and tested in real conditions. Critical challenges have also been identified and addressed.
Passive localization by multiple observes using ambiguous phase difference of arrival (PDOA) of the long baseline interferometer (LBI) can greatly reduce the cost and system complexity when applied to the small platform such as the unmanned aerial vehicle (UAV). To solve the nonlinear and discontinuous problem caused by the 2π ambiguity of PDOA, a quantum-behaved particle swarm optimization (QPSO)-based solution is proposed in this paper. The object function is firstly established using wrapped residual of the PDOA. And then the QPSO algorithm is applied to solve the nonlinear and discontinuous optimization problem. Simulation results verify the effectiveness of the proposed method.
Cache resource allocation and computation offloading are envisioned as two promising solutions to reduce maximal delay in fog radio access networks (F-RANs). Inspired by this, a joint cache and computation resource optimization problem is investigated in this paper. We propose a genetic algorithm-based approach that encodes the optimization order rather than the decision into genes and optimizes the delay stepwise. Furthermore, considering that the running time of the algorithm increases rapidly with the size of the network, a strategy with low time complexity is demanded. To achieve distributed cooperation among agents and reduce the complexity, we innovatively design a multi-agent game-based algorithm so that the original problem is decomposed into a priority calculation problem and several distributed sub-problems. Based on calculated priorities, players adjust their decisions in turn to improve their own delay until no player changes actions. Via simulation, the effectiveness of two proposed algorithms in reducing the delay is verified and the game-based algorithm shows lower time complexity and competitive performance. In addition, the impact of nodes’ storage and computation capability on delay performance is demonstrated and analyzed.
To overcome the large noise and harmonics in the current waveform of BLDC (Brushless Direct Current, BLDC) motor used in traditional automated guided vehicle (AGV), the permanent magnet synchronous motor(PMSM) is selected as drive motor to be introduced in the AGV. And an improved active disturbance rejection control (ADRC) algorithm are presented in the PMSM vector control. And three parts of ADRC, Tracking Differentiator (TD), Extended State Observer (ESO) and Nonlinear State Error Feedback (NLSEF), are designed in the controller to analyze and process the speed, voltage signal and state estimation values and control and compensate parameter errors. To show the control superiority of ADRC, the PI-based and ADRC-based simulation models are both built and simulated with the specific high and low speed, torque and speed suddenly change. Simulation results show that when the motor suddenly decelerates, PI is stable at 0.1s and ADRC was stable at 0.03s, and the overshoot of PI is higher than ADRC, which verifies that the response speed, anti-disturbance performance and robustness of the improved ADRC are significantly better than PI, which would be helpful to introduce the PMSM and ADRC based algorithm to the AGV and the automated container terminals.
