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The need of new curricula in the Internet of things (IoT) for MSc, PhD and engineering levels of education is described. The joint project on curricula development ALIOT, financed in the frame of Erasmus+ program, is discussed. The project ensures adaptation of academic programs in Ukraine and other countries to the needs of the labor market in the EU, thus influencing on the expansion of the opportunities...
Approximations and redundancies allow mobile and distributed applications to produce answers or outcomes of lesser quality at lower costs. This paper introduces RAPID, a new programming framework and methodology for service-based applications with approximations and redundancies. Finding the best service configuration under a given resource budget becomes a constrained, dual-weight graph optimization...
Indoor object recognition is a key task for mobile robot indoor navigation. In this paper, we proposed a pipeline for indoor object detection based on convolutional neural network (CNN). With the proposed method, we first pre-train an off-line CNN model by using both public Indoor Dataset and private frames of videos (FoV) dataset. This is then followed by a selective search process to extract a region...
Cerebral palsy (CP) is the most common children and adults movement disorder that is associated with life-long disability and multiple impairments. Children with CP demonstrate poor fine and gross motor function due to psychomotor disturbances. Early rehabilitation programs are essential for children with CP and should be appropriate for the age and functional condition of the patients. Robot-assisted...
The aim of this work is to detect diseases that occur on plants in tomato fields or in their greenhouses. For this purpose, deep learning was used to detect the various diseases on the leaves of tomato plants. In the study, it was aimed that the deep learning algorithm should be run in real time on the robot. So the robot will be able to detect the diseases of the plants while wandering manually or...
Many applications in audio signal processing require a precise identification of time frames where a predefined target source is active. In previous work, Artificial Neural Networks (ANNs) with crosscorrelation features showed a considerable potential in this field. In this paper, the performance of ANN-based target activity detection is analyzed in more detail and compared with a well-performing...
In many countries, robots and automation techniques are being introduced in agriculture farms to reduce the human labour and to improve the yield. However, such technological initiatives are still lacking in India, although it is the leading producer of many vegetables and fruits, for example, coconuts. Some of the activities carried out in a coconut farm that requires human labor are coconut dehusking,...
Interactions with simulated robots are typically presented on screens. Virtual reality (VR) offers an attractive alternative as it provides visual cues that are more similar to the real world. In this paper, we explore how virtual reality mediates human-robot interactions through two user studies. The first study shows that in situations where perception of the robot is challenging, a VR display provides...
Identifying an object of interest, grasping it, and handing it over are key capabilities of collaborative robots. In this context we propose a fast, supervised learning framework for learning associations between human hand gestures and the intended robotic manipulation actions. This framework enables the robot to learn associations on the fly while performing a task with the user. We consider a domestic...
This research paper explores the possibility of using Electromyogram (EMG) signals for classifying point to point upper limb movements during dynamic muscle contraction in the context of human-robot interactions. Previous studies have mostly focused on classifiers for gesture recognition using steady state EMG. Only few studies have used non-steady-state EMG classifier when gross upper arm muscles...
This study presents an age and gender estimation system that considers ethnic difference in face images using a Convolutional Neural Network(CNN) and Support Vector Machine(SVM). Most age and gender estimation systems using face images are trained on ethnicity-biased databases. Therefore, these systems show limited performance on face images of ethnic groups occupying a small proportion of the training...
The goal of the Internet of Things (IoT) is to transform any thing around us, such as a trash can or a street light, into a smart thing. A smart thing has the ability of sensing, processing, communicating and/or actuating. In order to achieve the goal of a smart IoT application, such as minimizing waste transportation costs or reducing energy consumption, the smart things in the application scenario...
Localizing functional regions of objects or affordances is an important aspect of scene understanding and relevant for many robotics applications. In this work, we introduce a pixel-wise annotated affordance dataset of 3090 images containing 9916 object instances. Since parts of an object can have multiple affordances, we address this by a convolutional neural network for multilabel affordance segmentation...
We present DeepNav, a Convolutional Neural Network (CNN) based algorithm for navigating large cities using locally visible street-view images. The DeepNav agent learns to reach its destination quickly by making the correct navigation decisions at intersections. We collect a large-scale dataset of street-view images organized in a graph where nodes are connected by roads. This dataset contains 10 city...
This is a placeholder for the invited session presenting the results of the Data Fusion Contest 2017. Papers in the session do not enter the usual review process. Instead, winners of the 2017 Data Fusion Contest will write 4-page papers evaluated by the Contest Committee, that will be submitted as camera-ready papers in due date. So, DO NOT REVIEW THIS PAPER. If by mistake it enters the usual review...
While there has been recent success with robotic therapy approaches, individual differences in motor impairments motivate the need for customized therapy. Our latest work with healthy participants considered the likelihood of one's error to construct a customized force field training environment, which we termed an error field. We believe error statistics could characterize individual motor impairments...
The combined use of Functional Electrical Stimulation (FES) and robotic technologies is advocated to improve rehabilitation outcomes after stroke. This work describes an arm rehabilitation system developed within the European project RETRAINER. The system consists of a passive 4-degrees-of-freedom exoskeleton equipped with springs to provide gravity compensation and electromagnetic brakes to hold...
The authors are developing a talking robot which is a mechanical vocalization system modeling the human articulatory system. The talking robot is constructed with mechanical parts that are made by referring to human vocal organs biologically and functionally. In this study, a newly redesign artificial vocal cord is developed for the purpose of extending the speaking capability of the talking robot...
Uniform test suites consist of test cases exclusively differing in test inputs - not in test goals. Intended to gain confidence that a given invariant holds, these inputs trigger particular behavior of the system under test. Equipped with a simulation of the system under test we are able to cheaply explore this behavior virtually. When changing over to reality, testing the system within its real context,...
In daily life it is necessary to learn skills that can be applied in different tasks and different contexts. Usually these skills are acquired by observation or by direct physical training with another expert person. The critical point is to know which is the best possible way to achieve this knowledge acquisition. In this work we have proposed a collaborative environment where subjects with different...
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