OBSERVATION CONTROL DEVICE FOR THE URAN-4 DECAMETER RADIO TELESCOPE

The URAN-4 radio telescope has been operating at the Odessa Radio Astronomy Observatory of the Radio Institute of the National Academy of Sciences of Ukraine since 1986. The telescope is an element of a decameter long-base interferometer – the radio telescopes which are located across the territory of Ukraine from west to east. The URAN-4 consists of antenna and upgraded radiometer. Antenna consists of 128–element with phased array with dimensions of 232.5 x 22.5 m. The telescope operates in the 10 – 30 MHz range. Its receiving equipment is capable of separating two polarization components of the received signal. The radiation pattern of the radio telescope is 2,7x22 degrees at 25 MHz. The resolution of 2 seconds is realized in the interferometer mode. The instrumental complex of the radio telescope includes the upgraded device for controlling its operation. This device is made in the form of a separate unit. The ATMEL AT90S8515 microcontroller with registers and a communication circuit between the unit and the computer are mounted in it. The discrete movement in space of the antenna's directional pattern and sets the required attenuation in the attenuator during calibration are carried out with the upgraded controlling device at a given time during the observation period. The controlling device also allows adjust the current time of the computer using GPS.

De novo learning versus adaptation of continuous control in a manual tracking task

How do people learn to perform tasks that require continuous adjustments of motor output, like riding a bicycle? People rely heavily on cognitive strategies when learning discrete movement tasks, but such time-consuming strategies are infeasible in continuous control tasks that demand rapid responses to ongoing sensory feedback. To understand how people can learn to perform such tasks without the benefit of cognitive strategies, we imposed a rotation/mirror reversal of visual feedback while participants performed a continuous tracking task. We analyzed behavior using a system identification approach which revealed two qualitatively different components of learning: adaptation of a baseline controller and formation of a new, task-specific continuous controller. These components exhibited different signatures in the frequency domain and were differentially engaged under the rotation/mirror reversal. Our results demonstrate that people can rapidly build a new continuous controller de novo and can simultaneously deploy this process with adaptation of an existing controller.

Detection of Hostile Intent by Spatial Movements

Objective The ability of people to infer intentions from movement of other vessels was investigated. Across three levels of variability in movements in the path of computer-controlled ships, participants attempted to determine which entity was hostile. Background Detection of hostile intentions through spatial movements of vessels is important in an array of real-world scenarios. This experiment sought to determine baseline abilities of humans to do so. Methods Participants selected a discrete movement direction of their ship. Six other ships’ locations then updated. A single entity displayed one of two hostile behaviors: shadowing, which involved mirroring the participant’s vessel’s movements; and hunting, which involved closing in on the participant’s vessel. Trials allowed up to 35 moves before identifying the hostile ship and its behavior. Uncertainty was introduced through adding variability to ships’ movements such that their path was 0%, 25%, or 50% random. Results Even with no variability in the ships’ movements, accurate detection was low, identifying the hostile entity about 60% of the time. Variability in the paths decreased detection. Detection of hunting was strongly degraded by distance between ownship and the hostile ship, but shadowing was not. Strategies employing different directions of movement across the trial, but also featuring some runs of consecutive movements, facilitated detection. Conclusions Early identification of threats based on movement characteristics alone is likely to be difficult, but particularly so when adversaries employ some level of uncertainty to mask their intentions. These findings highlight the need to develop decision aids to support human performance.

Stepper induction motors for electric drive

Aim: The goal is to present a new asynchronous principle of operation of stepper motors, based on the use of counter-rotating (or traveling) magnetic fields. Method: A change in the degree of symmetry of one of these magnetic fields leads to the fact that the armature (rotor or secondary element) makes a precise discrete movement. Result: The force moving the armature of a stepper induction motor is created as a result of the interaction of eddy currents in the armature with a rotating or traveling magnetic field. Stepper induction motors can rotate the rotor at a certain angle and discretely move flat or cylindrical electrically conductive armatures, which can be smooth and non-magnetic. A separate group of motors for a discrete electric drive is made up of two-coordinate linear stepping asynchronous machines, which also operate using the same counter-running magnetic fields both in the longitudinal and transverse directions. Conclusion: The features of the design of such electric machines are presented, the values of the magnetic induction in different zones of a two-coordinate stepper motor are determined, the relations for calculating the steps of the armature in both the longitudinal and transverse directions are given.

Differences in Facial Expressions between Spontaneous and Posed Smiles: Automated Method by Action Units and Three-Dimensional Facial Landmarks

Research on emotion recognition from facial expressions has found evidence of different muscle movements between genuine and posed smiles. To further confirm discrete movement intensities of each facial segment, we explored differences in facial expressions between spontaneous and posed smiles with three-dimensional facial landmarks. Advanced machine analysis was adopted to measure changes in the dynamics of 68 segmented facial regions. A total of 57 normal adults (19 men, 38 women) who displayed adequate posed and spontaneous facial expressions for happiness were included in the analyses. The results indicate that spontaneous smiles have higher intensities for upper face than lower face. On the other hand, posed smiles showed higher intensities in the lower part of the face. Furthermore, the 3D facial landmark technique revealed that the left eyebrow displayed stronger intensity during spontaneous smiles than the right eyebrow. These findings suggest a potential application of landmark based emotion recognition that spontaneous smiles can be distinguished from posed smiles via measuring relative intensities between the upper and lower face with a focus on left-sided asymmetry in the upper region.

De novo learning and adaptation of continuous control in a manual tracking task

How do people learn to perform tasks that require continuous adjustments of motor output, like riding a bicycle? People rely heavily on cognitive strategies when learning discrete movement tasks, but such time-consuming strategies are infeasible in continuous control tasks that demand rapid responses to ongoing sensory feedback. To understand how people can learn to perform such tasks without the benefit of cognitive strategies, we imposed a rotation/mirror reversal of visual feedback while participants performed a continuous tracking task. We analyzed behavior using a system identification approach which revealed two qualitatively different components of learning: adaptation of a baseline controller and formation of a new task-specific continuous controller. These components exhibited different signatures in the frequency domain and were differentially engaged under the rotation/mirror reversal. Our results demonstrate that people can rapidly build a new continuous controller de novo and can flexibly integrate this process with adaptation of an existing controller.