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This paper proposes a novel architecture for control systems of modular reconfigurable robots that combines centralized and decentralized approaches to achieve the best ration between efficiency, fault-tolerance and cost-efficiency.
In the realm of surface electromyography (sEMG) gesture recognition, deep learning algorithms are seldom employed. This is due in part to the large quantity of data required for them to train on. Consequently, it would be prohibitively time consuming for a single user to generate a sufficient amount of data for training such algorithms. In this paper, two datasets of 18 and 17 able-bodied participants...
This paper proposes and analyzes the use of the Arduino Zero board as the lab platform for the Computer Structure course that constitutes an essential part of Computer Science studies. The understanding of the main functional blocks of a computer, addressing the main concepts included in the course syllabus, is reinforced by mean of the hands-on experience acquired in the lab sessions and the completion...
Humans often use indirect speech acts (ISAs) when issuing directives. Much of the work in handling ISAs in computational dialogue architectures has focused on correctly identifying and handling the underlying non-literal meaning. There has been less attention devoted to how linguistic responses to ISAs might differ from those given to literal directives and how to enable different response forms in...
In this paper we address the issue of connecting abstract task definitions at a mission level with control functionalities for the purpose of performing autonomous robotic missions using multiple heterogenous platforms. The heterogeneity is handled by the use of a common vocabulary which consists of parametrized tasks such as fly-to, take-off, scan-area, or land. Each of the platforms participating...
The world is moving towards to the fourth industrial revolution, usually linked with the Industrie 4.0 initiative, enables the digitization of manufacturing factories by using Cyber-Physical Systems and emergent technologies like Internet of Things and Internet of Services. The seamless reconfiguration of these complex industrial cyber-physical systems is an important challenge for the complete implementation...
Heterogeneity of current software solutions for 5G is heading for complex and costly situations, with high fragmentation, which in turn creates uncertainty and the risk of delaying 5G innovations. This context motivated the definition of a novel Operating Platform for 5G (5G-OP), a unifying reference functional framework supporting end-to-end and multi-layer orchestration. 5G-OP aims at integrated...
Controlling a bicycle without human interaction is still a challenge for researchers. Most of the studies on this topic focus on the physical area of bicycle or designing controllers based on automatic control knowledge such as feedback controller, LQR controller. This study focuses on applying a state-of-the-art deep reinforcement learning algorithm called Deep Deterministic Policy Gradient to control...
The progress of robotic technology in the last years paved the way for its application in the healthcare sector. In the light of demographic change and lack of nursing staff, telerobotic systems are a promising solution e.g. for homecare in rural areas or in quarantine stations to prevent dissemination of pathogens. This paper presents a user study investigating human performance when teleoperating...
Hierarchical Temporal Memory (HTM) is a computational model of the neocortex that is capable of online learning to predict and detect anomalies from continuous data streams. To make HTM also available on power-constrained robot systems, we investigate the feasibility of implementing the model on SpiNNaker, a fully programmable energy-efficient neuromorphic many core system. Our contribution is twofold:...
Routers in Content-Centric Networking (CCN) may locally cache frequently requested content in order to speed up delivery to end users. Thus, the issue of caching strategies arises, i.e., which content shall be stored and when it should be replaced. In this work, we employ, and study the feasibility of, novel techniques towards intelligent control of CCN routers that autonomously switch between existing...
This paper deals with the Advanced Command and Control System (AC2S) which has been developed at University of Defence, Brno, Czech Republic. AC2S represents a new concept of using modern technologies to increase the efficiency of military operations. The first part of the article discusses the architecture of AC2S from the system and communication point of view. The next part presents the concept...
Convolutional neural networks (CNN) are a deep learning technique that has achieved state-of-the-art prediction performance in computer vision and robotics, but assume the input data can be formatted as an image or video (e.g. predicting a robot grasping location given RGB-D image input). This paper considers the problem of augmenting a traditional CNN for handling image-like input (called main-channel...
In this paper we propose a new hardware architecture for the implementation of an artificial neuron based on organic memristive elements and operational amplifiers. This architecture is proposed as a possible solution for the integration and deployment of the cluster based bio- realistic simulation of a mammalian brain into a robotic system. Originally, this simulation has been developed through a...
In this paper, we investigate online nonlinear regression and introduce novel algorithms based on the long short term memory (LSTM) networks. We first put the underlying architecture in a nonlinear state space form and introduce highly efficient particle filtering (PF) based updates, as well as, extended Kalman filter (EKF) based updates. Our PF based training method guarantees convergence to the...
Clean energy production increment is being led by advancements in photovoltaic plants, both for on-grid power generation, and for local usage (farms, industrial plants, etc.). The main challenge faced by PV plants is the dreadful reduction of power production caused by the accumulation of dirt and dust on the panels. This effect has a higher magnitude in areas where dust storms or dusty environments...
The abstraction tasks are challenging for multi-modal sequences as they require a deeper semantic understanding and a novel text generation for the data. Although the recurrent neural networks (RNN) can be used to model the context of the time-sequences, in most cases the long-term dependencies of multi-modal data make the back-propagation through time training of RNN tend to vanish in the time domain...
Since the 21st century, vehicles have attracted a great interest due to their potential applications for transportation of people and goods. The initial concerns of industries and researchers were that radio-equipped vehicles are able to keep the drivers informed about risks and road conditions. However, recent researches focuses more on providing the drivers with more comfort an less effort. For...
Anthropocentrism is an innate socio-cultural trait of human behavior, which influences our everyday activities and social interactions. For this reason, it is reasonable to assume that anthropocentrism also influences software design. We define anthropocentrism in software design as viewing and interpreting every software design aspect in terms of human experience and values. This phenomenon becomes...
Robotics has adopted modeling with architecture description languages (ADLs). This introduces a gap when reusing solutions encoded in middleware modules. Existing ADL modeling in robotics focuses on domain challenges instead of tool modularity, hence customizing an ADL tool to generate solutions conforming to a specific middleware (e.g., ROS) is challenging. This could produce a multitude of incompatible...
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