105,863 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
A MANAGED APPROACH OF INTERACTION BETWEEN AGILE SCRUM AND SOFTWARE CONFIGURATION MANAGEMENT SYSTEM
In current age the agile software development is one of the most popular software development methodology but due the mismanagement and lack of efficient handling of agile scrum and software configuration management system our software industry is facing a high rate of failed product, keeping this as my motivation, I have designed a efficient checklist which will help the industry to organized the interaction between agile scrum process and software configuration management system in a efficient and managed way and definitely that will increase the successful project in the software industry. Index-term : Agile Scrums, Software development, Software configuration management system, Checklist, Successful project
Age-related differentiation of sensorimotor control strategies during pursuit and compensatory tracking
Motor control deficits during aging have been well-documented. Various causes of neuromotor decline, including both peripheral and central neurological deficits, have been hypothesized. Here, we use a model of closed-loop sensorimotor control to examine the functional causes of motor control deficits during aging. We recruited 14 subjects aged 19-61 years old to participate in a study in which they performed single-joint compensatory and pursuit tracking tasks with their dominant hand. We found that visual response delay and visual noise increased with age, while reliance on visual feedback, especially during compensatory tracking decreased. Increases in visual noise were also positively correlated with increases in movement error during a reach and hold task. The results suggest an increase in noise within the visuomotor control system may contribute to the decline in motor performance during early aging
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself are crucial. In this work, we propose a novel framework for
probabilistic online motion planning with online adaptation based on a
bio-inspired stochastic recurrent neural network. By using learning signals
which mimic the intrinsic motivation signalcognitive dissonance in addition
with a mental replay strategy to intensify experiences, the stochastic
recurrent network can learn from few physical interactions and adapts to novel
environments in seconds. We evaluate our online planning and adaptation
framework on an anthropomorphic KUKA LWR arm. The rapid online adaptation is
shown by learning unknown workspace constraints sample-efficiently from few
physical interactions while following given way points.Comment: accepted in Neural Network
Scalable software architecture for on-line multi-camera video processing
In this paper we present a scalable software architecture for on-line multi-camera video processing, that guarantees a good trade off between computational power, scalability and flexibility. The software system is modular and its main blocks are the Processing Units (PUs), and the Central Unit. The Central Unit works as a supervisor of the running PUs and each PU manages the acquisition phase and the processing phase. Furthermore, an approach to easily parallelize the desired processing application has been presented. In this paper, as case study, we apply the proposed software architecture to a multi-camera system in order to efficiently manage multiple 2D object detection modules in a real-time scenario. System performance has been evaluated under different load conditions such as number of cameras and image sizes. The results show that the software architecture scales well with the number of camera and can easily works with different image formats respecting the real time constraints. Moreover, the parallelization approach can be used in order to speed up the processing tasks with a low level of overhea
Online Multi-task Learning with Hard Constraints
We discuss multi-task online learning when a decision maker has to deal
simultaneously with M tasks. The tasks are related, which is modeled by
imposing that the M-tuple of actions taken by the decision maker needs to
satisfy certain constraints. We give natural examples of such restrictions and
then discuss a general class of tractable constraints, for which we introduce
computationally efficient ways of selecting actions, essentially by reducing to
an on-line shortest path problem. We briefly discuss "tracking" and "bandit"
versions of the problem and extend the model in various ways, including
non-additive global losses and uncountably infinite sets of tasks
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