Video Game Training and Reaction Time Skills Among Older Adults

The aim of the current study was to examine whether reaction time (RT) among older adults could be improved through video game training. Twenty-nine participants between the ages of 50 and 84 either played (N = 14) or did not play (N = 15) a video game, Pac-Man: Adventures in Time. All participants completed a pre- and postassessment of RT. Results from this study showed that there were no significant differences between the control and experimental groups on RT performance across time. These findings indicate that the amount of training provided may have been inadequate to improve RT skills. It is therefore important to investigate the amount of training required to allow older adults to respond faster to environmental stimuli and transfer skills to activities of daily living.

Robust nonlinear distributed controller design for active and reactive power sharing in islanded microgrids

This paper presents a robust nonlinear distributed controller design for islanded operation of microgrids in order to maintain active and reactive power balance. In this paper, microgrids are considered as inverter-dominated networks integrated with renewable energy sources (RESs) and battery energy storage systems (BESSs), where solar photovoltaic generators act as RESs and plug-in hybrid electric vehicles as BESSs to supply power into the grid. The proposed controller is designed by using partial feedback linearization and the robustness of this control scheme is ensured by considering structured uncertainties within the RESs and BESSs. An approach for modeling the uncertainties through the satisfaction of matching conditions is also provided in this paper. The proposed distributed control scheme requires information from local and neighboring generators to communicate with each other and the communication among RESs, BESSs, and control centers is developed by using the concept of the graph theory. Finally, the performance of the proposed robust controller is demonstrated on a test microgrid and simulation results indicate the superiority of the proposed scheme under different operating conditions as compared to a linear-quadratic-regulator-based controller.

Robust nonlinear controller design for three-phase grid-connected photovoltaic systems under structured uncertainties

This paper presents a robust nonlinear controller design for a three-phase grid-connected photovoltaic (PV) system to control the current injected into the grid and the dc-link voltage for extracting maximum power from PV units. The controller is designed based on the partial feedback linearization approach, and the robustness of the proposed control scheme is ensured by considering structured uncertainties within the PV system model. An approach for modeling the uncertainties through the satisfaction of matching conditions is provided. The superiority of the proposed robust controller is demonstrated on a test system through simulation results under different system contingencies along with changes in atmospheric conditions. From the simulation results, it is evident that the robust controller provides excellent performance under various operating conditions. © 2014 IEEE.

ANFIS traffic signal controller for an isolated intersection

Traffic signal controlling is one of the solutions to reduce the traffic congestion in cities. To set appropriate green times for traffic signal lights, we have applied Adaptive Neuro-Fuzzy Inference System (ANFIS) method in traffic signal controllers. ANFIS traffic signal controller is used for controlling traffic congestion of a single intersection with the purpose of minimizing travel delay time. The ANFIS traffic controller is an intelligent controller that learns to set an appropriate green time for each phase of traffic signal lights at the start of the phase and based on the traffic information. The controller uses genetic algorithm to tune ANFIS parameters during learning time. The results of the experiments show higher performance of the ANFIS traffic signal controller compared to three other traffic controllers that are developed as benchmarks. One of the benchmarks is GA-FLC (Araghi et al., 2014), next one is a fixed-FLC, and a fixed-time controller with three different values for green phase. Results show the higher performance of ANFIS controller.

A sliding-mode-control-based adaptive cruise controller

This paper presents an alternative solution to the conventional cruise controller of a hybrid electric vehicle based on the sliding mode control approach. The mathematical model of a hybrid electric vehicle cruise control system is developed. Then, the sliding mode control approach is applied as the controller. The sliding mode control stability is investigated and demonstrated. Thereafter, the system is simulated and the results are presented. © 2014 IEEE.

Low cost MPPT controller for a photovoltaic-based microgrid

 Photovoltaic based microgrid have been increasingly investigated in recent years, ascribable to their fundamental advantages such as the infinite energy source, environmentally friendly aspect and low upkeep cost. However, in practice, they are still considered as an expensive and low output option of renewable energy resources. To extract the maximum possible power from the output of the PV system, a reliable maximum power point tracker (MPPT) is required. Numerous studies have been conducted to introduce the best MPPT techniques suitable for different types of PV systems. However, they are mostly able to track the MPP from the PV system when the output signals (Voltage and Current) of individual array are available. In this study, a meta-heuristic method, based on particle swarm optimization theory, is used to determine the actual MPP of PV system, including several PV arrays, by only single current sensor at the output terminal. The results of the proposed PSO based technique, for tracking the global MPP in a multidimensional search space, have been presented at the end of this paper.

Optimal design of traffic signal controller using neural networks and fuzzy logic systems

This paper aims at optimally adjusting a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. Neural network (NN) and fuzzy logic system (FLS) are two methods applied to develop intelligent traffic timing controller. For this purpose, an intersection is considered and simulated as an intelligent agent that learns how to set green times in each cycle based on the traffic information. The training approach and data for both these learning methods are similar. Both methods use genetic algorithm to tune their parameters during learning. Finally, The performance of the two intelligent learning methods is compared with the performance of simple fixed-time method. Simulation results indicate that both intelligent methods significantly reduce the total delay in the network compared to the fixed-time method.

Optimal fuzzy traffic signal controller for an isolated intersection

  This paper aims at optimally adjusting a set of green times for traffic lights in a single intersection with the purpose of minimizing travel delay time and traffic congestion. Fuzzy logic system (FLS) is the method applied to develop the intelligent traffic timing controller. For this purpose, an intersection is considered and simulated as an intelligent agent that learns how to set green times in each cycle based on the traffic information. The FLS controller (FLC) uses genetic algorithm to tune its parameters during learning phase. Finally, The performance of the intelligent FLC is compared with the performance of a FLC with predefined parameters and three simple fixed-time controller. Simulation results indicate that intelligent FLC significantly reduces the total delay in the network compared to the fixed-time method and FLC with manual parameter setting.

Robotic grasping and manipulation controller framework: architecture redevelopment

This paper details the further improvements obtained by redesigning a previously offered Manipulation Controller Framework to provide support to an innovative, friction-based object slippage detection strategy employed by the robotic object manipulator. This upgraded Manipulation Controller Framework includes improved slippage detection functionality and a streamlined architecture designed to improve controller robustness, reliability and speed. Improvements include enhancements to object slippage detection strategy, the removal of the decision making module and integration of its functionality into the Motion Planner, and the stream-lining of the Motion Planner to improve its effectiveness. It is anticipated that this work will be useful to researchers developing integrated robot controller architectures and slippage control.

Game of Tropes: the Orientalist tradition in the Works of G.R.R. Martin

George R.R. Martin's A Song of Ice and Fire novels (and their television adaption, Game of Thrones) have become arguably the most well known fantasy epic of the last decade. However, the world of A Song of Ice and Fire conforms to many of the same Orientalist tropes that have dominated Western literature since the popularisation of the 'Arabian fantasy' in the 18th and 19th centuries and its subsequent perpetuation in film and television. Derivative imaginings of the real world Middle East are commonly reflected in non-Earthly fantasy worlds and Martin's work incorporates this standard vision of the Eastern Other. Owing to its popularity, the A Song of Ice and Fire series represents a significant reinforcement of Orientalist stereotypes and proves that fantasy locations have significant power to cement these ideas in the popular imagination. Moreover, the negative portyal of the East in these works supports Said's argument that the Orient is an invention of the West, and that our depiction of the Other is a means of framing our own cultural superiority.

Does playing a sports active video game improve young children's ball skill competence?

OBJECTIVES: Actual and perceived object control (commonly ball) skill proficiency is associated with higher physical activity in children and adolescents. Active video games (AVGs) encourage whole body movement to control/play the electronic gaming system and therefore provide an opportunity for screen time to become more active. The purpose of this study was to determine whether playing sports AVGs has a positive influence on young children's actual and perceived object control skills. DESIGN: Two group pre/post experimental design study. METHODS: Thirty-six children aged 6-10 years old from one school were randomly allocated to a control or intervention condition. The Test of Gross Motor Development-3 assessed object control skill. The Pictorial Scale of Perceived Competence for Young Children assessed perceived object control skill. The intervention consisted of 6×50min lunchtime AVG sessions on the Xbox Kinect. Two to three sport games were chosen for participants to play each session. General linear models with either perceived object control or actual object control skill as the outcome variables were conducted. Each base model adjusted for intervention status and pre-score of the respective outcome variable. Additional models adjusted for potential confounding variables (sex of child and game at home). RESULTS: No significant differences between the control and intervention groups were observed for both outcomes. CONCLUSIONS: This study found that playing the Xbox Kinect does not significantly influence children's perceived or actual object control skills, suggesting that the utility of the Xbox Kinect for developing perceived and actual object control skill competence is questionable.