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Studies using zebrafish (Danio rerio) in neuro-behavioural research are growing. Measuring fish behavior by computational methods is one of the most efficient ways to avoid human bias in experimental analyses, extending them to various approaches. Sometimes, thorough analyses are difficult to do, as fish can behave unpredictably during an experimental strategy. However, the analyses can be implemented in an automated way, using an online strategy and video processing for a complete assessment of the zebrafish behavior, based on the detection and tracking of fish during an activity. Here, a fully automatic conditioning and detailed analysis of zebrafish behavior is presented. Microcontrolled components were used to control the delivery of visual and sound stimuli, in addition to the concise amounts of food after conditioned stimuli for adult zebrafish groups in a conventional tank. The images were captured and processed for automatic detection of the fish, and the training of the fish was done in two evaluation strategies: simple and complex. In simple conditioning, the zebrafish showed significant responses from the second attempt, learning that the conditioned stimulus was a predictor of food presentation in a specific space of the tank, where the food was dumped. When the fish were subjected to two stimuli for decision-making in the food reward, the zebrafish obtained better responses to red light stimuli in relation to vibration. The behavior change was clear in stimulated fish in relation to the control group, thus, the distances traveled and the speed were greater, while the polarization was lower in stimulated fish. This automated system allows for the conditioning and assessment of zebrafish behavior online, with greater stability in experiments, and in the analysis of the behavior of individual fish or fish schools, including learning and memory studies.

In this study, we explore the difficulties of students in the disciplines of post-graduation in electrical engineering. To the extent that the student is able to elucidate his difficulties during the disciplines of the postgraduate course, your research can flow with greater satisfaction and success. Our findings are based on interviews of students with different backgrounds and educational experiences, allowing to capture different difficulties and motivations found in the classroom, which influence the researches of masters and doctoral students. We found that most of the students in the postgraduate course in electrical engineering had background training in distinct areas (73.3%), and that they are generally related area students, such as math, computing, and other areas of engineering. Another aspect is that most interviewees reported that their difficulties were related to the disciplines that addressed the development of algorithms and mathematical calculations (66%), suggesting that this problem was a consequence of insufficient knowledge base for the disciplines. The findings suggest that even with the difficulties encountered in the classroom, the students of the course had no disapproval, because most of the time they sought to discuss their difficulties in groups of studies created by classmates, and thus, elucidating the difficulties faced with colleagues who had different skills.

Fish show rapid movements in various behavioral activities or associated with the presence of food. However, in periods of rapid movement, the rate at which occlusion occurs among the fish is quite high, causing inconsistency in the detection and tracking of fish, hindering the fish's identity and behavioral trajectory over a long period of time. Although some algorithms have been proposed to solve these problems, most of their applications were made in groups of fish that swim in shallow water and calm behavior, with few sudden movements. To solve these problems, a convolutional network of object recognition, YOLOv2, was used to delimit the region of the fish heads to optimize individual fish detection. In the tracking phase, the Kalman filter was used to estimate the best state of the fish's head position in each frame and, subsequently, the trajectories of each fish were connected among the frames. The results of the algorithm show adequate performances in the trajectories of groups of zebrafish that exhibited rapid movements.

In this paper, we describe an architectural framework by which a mobile robot can learn how to autonomously assemble solid 3D brick structures according to user-specified designs. The policies of actions to perform the construction task are obtained from a simulation environment using Reinforcement Learning (RL) and Particle Swarm Optimization (PSO) approaches. The proposed planning architecture is used to simultaneously solve three problems: 1) to generate feasible construction policies, 2) to define the set of maneuvers for the vehicle to carry out the assembly task, and 3) to obtain the set of trajectories for handling and mounting parts while avoiding fixed obstacles. During the learning process the power limitation of the ground robot is taken into account. Simulation results show that the set of learned actions may efficiently perform the construction procedures without resulting in conditions which prevent the fulfillment of the assembly procedures. The synthesis of this system opens the way to the development of intelligent construction approaches using ground robots.

This work presents the development and imple- mentation of an autonomous construction system which uses a holonomic mobile robot for constructing three-dimensional modular structures from parts of different size. The main contribution of this paper is to propose a novel architectural framework for planning the construction task by reducing the total traveled distance and learning time with respect to the state of the art. A high-level planner based on Parameterized Learning Automata (PLA) and $\mathrm {A}^{\ast }$ algorithms uses environmental feedback to generate the robot actions for accomplishing the task. The robotic platform is equipped with a magnetic gripper and also a RGB-D camera and other additional sensors to aid the control and navigation of the robot. The simultaneous mapping and robot location are performed using the Real-Time Appearance-Based Mapping (RTAB-Map) algorithm. The RTAB-Map algorithm uses three-dimensional image information and odometry data to create the map environment and also to estimate the pose of the robot. Simulation and experimental trials have been performed to assess the effectiveness of the proposed robotic assembly system.