Soil Sensor Network

Water management during crop irrigation is a problem for the agricultural industry. To help farmers better maintain water usage, a wireless soil sensor network comprised of a sensor pod and wireless communication has been designed and implemented. It was proven that the sensor pod can be installed 6-8 inches below the ground and communicate up to at least a 6km distance back to the gateway. The senor pod shells have a 2 mm thick shell to prevent the pod from shattering when coming into contact with the ground after being released from the planter, as calculated through the force of impact equations. The sensor pod contains a capacitive soil moisture sensor with an accuracy of 90% and a temperature sensor with an accuracy of ±0.2ºC. Lithium-ion batteries with a 2800 mA-H rating were chosen to ensure the sensor pods would be power-efficient in order to last an entire growing season. The sensor data is transmitted wirelessly through LoRaWAN communication using a RN2903 transceiver and a quarter wavelength, 3” monopole antenna. A Sentrius Laird gateway was used to collect and forward sensor pod data to the Senet dashboard. The Senet dashboard then forwarded the data to a web-based application that farmers can reference to check the status of their fields

Nonlinear analysis of EEG signals at different mental states

BACKGROUND: The EEG (Electroencephalogram) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer can not directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. This work discusses the effect on the EEG signal due to music and reflexological stimulation. METHODS: In this work, nonlinear parameters like Correlation Dimension (CD), Largest Lyapunov Exponent (LLE), Hurst Exponent (H) and Approximate Entropy (ApEn) are evaluated from the EEG signals under different mental states. RESULTS: The results obtained show that EEG to become less complex relative to the normal state with a confidence level of more than 85% due to stimulation. CONCLUSIONS: It is found that the measures are significantly lower when the subjects are under sound or reflexologic stimulation as compared to the normal state. The dimension increases with the degree of the cognitive activity. This suggests that when the subjects are under sound or reflexologic stimuli, the number of parallel functional processes active in the brain is less and the brain goes to a more relaxed stat

7-mi dimenzionální optimalizační úloha: PBO-Přírodními procesy inspirovaný optimalizátor versus 10 let starý algoritmue EPSDE

Origins of the branch of numerical optimizations with use of evolutionary opti-mizers date almost 60 years back. It is the area which does not evolve by big jumps and the advancement sometimes hits stagnation periods. At such stagna-tion times there is a big hungry for the new optimization methods which would fill up the empty space. Many optimizers have appeared in the last decade. These optimizers are more specialized in comparison to optimizers which were pro-posed dozens of years back. The new optimizers are very often derived from old-er optimizers. In this presented paper, a 7-dimensional optimization task is solved which is called persons identification using contour of a human hand. The paper is considered as research and comparative study at the same time. An optimizer called EPSDE is compared to the Polar Bear Optimizer. The EPSDE is a deputy of 3rd generation optimizers derived from algorithm differential evolution. The PBO falls into a group of young optimizers marked as „nature inspired“. The PBO is three times more time-demanding and primarily significantly worse in solving of given task which is very difficult. A comparison of both optimizers was conducted with use of large comparative database.Počátky evoluční numerické optimalizace spadají až do 60. let minulého století. Tato vědní oblast se rozhodně nevyvíjí velkými skoky a často se v průběhu let setkáváme i s obdobím stagnace. V obdobích stagnace je logicky velký hlad po nových metodách, které by zaplnily prázdné místo ve vývoji. V poslední dekádě se objevilo mnoho zajímavých optimalizátorů. Jsou více specializované v porovnání s těmi, které se objevily před desítkami let. V předkládaném článku je presentována komparativní studie nazvaná 7mi dimenzionální optimalizační úloha řešící identifikaci osob s využitím kontury lidské ruky. Studie je uvažována jako výzkumná i komparativní současně. Optimalizátor EPSDE je zde porovnáván vůči optimalizátoru PBO. EPSDE je zástupce třetí generace optimalizátorů odvozených od algoritmu diferenciální evoluce. PBO naopak spadá do kategorie nových, mladých a progresivních a přírodou inspirovaných optimalizátorů. PBO je téměř třikrát časově náročnější v porovnání s EPSDE a navíc danou úlohu řeší velmi špatně