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This paper show how neural networks, configured for regression, can be used to learn the relationships between Inertial Motion Unit (IMU) data collected on a robotic platform and the robot's commanded system state. By learning how the IMU data relates to commanded robot state we can use the neural network to predict what commands have been issued to the robot. By comparing the prediction to the actual...
This paper presents a system for classifying images based on Deep Learning and applied in the recognition of traffic signals aiming to increase road safety increased road safety using autonomous and semi-autonomous intelligent robotic vehicles. This Advanced Driver Assistance System (ADAS) is a system created to automate vehicles, but also to help the human drivers to increase safety and the respect...
A big challenge in the precision agriculture is the detection of fruits in coffee crops on agricultural environments. This paper presents a comparison of four features set to detect the red fruits (mature) in Coffee plants. An Unmanned Aerial Vehicle (UAV) is used to obtain high-resolution RGB images of a coffee hall. The proposed methodology enables the extraction of visual features from image regions...
In recent years, a great many robot-assisted therapy systems have been developed and applied in neural rehabilitation. In this paper, we develop a wearable upper limb exoskeleton robot for the purpose of assisting the disable patients to execute effective rehabilitation. The proposed exoskeleton system consists of 7 degrees of freedom (DOFs) and is capable of providing naturalistic assistance of shoulder,...
Stroke has become a common disease, often leading to motor dysfunction and causing disability. Lower-limb robotic rehabilitation can help patients to carry out reasonable and effective training to improve the motor learning. The developments of lower-limb rehabilitation robots are investigated in this review and also clinic requirements have been discussed in recent years. Consequently, the future...
According to pattern design of western-style clothes, the related body data of 3D human scanner acquisition is adpoted to establish BP neural network model for intelligent design of clothin gdesign. The selection, input variable and data preprocessing of sample data in the model are regulated, and the number of hidden layer neurons in the model is determined through experiments. By training and simulation...
This paper reports on three measurement science field exercises for evaluating ground, aerial, and aquatic robots. These events, conducted from February to June 2017, were conducted in close co-ordination with the responder community, standards organizations, manufacturers, and academia. Test data from a wide variety of robot platforms were gathered in a wide variety of standard and prototypical test...
Word-sense disambiguation is one of the key concepts in natural language processing. The main goal of a language is to present a specific concept to the audience. This concept is extracted from the meaning of words in that language. System should be able to identify role and meaning of words in order to identify the concepts in texts properly. This issue becomes more problematic if there are words...
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...
The process through which children learn about the world and develop perceptual, cognitive and motor skills relies heavily on object exploration in their physical world. New types of assistive technology that enable children with impairments to interact with their environment have emerged in recent years, and they could be beneficial for children's cognitive and perceptual skills development. Many...
A two-layer fuzzy kernel regression (TLFKR) model is proposed for understanding human emotional intention in human-robot interaction, where TLFKR model consists of two layers, including fuzzy c-means (FCM) with kernel ridge regression (Kernel 1) for information analysis layer, fuzzy support vector regressions (FSVR) (Kernel 2) for intention understanding layer. TLFKR model represents the weight impact...
In complex industrial environment, there are many interference objects when detect the objects on the production line. The interference objects are very similar with the objects to be sorted in terms of color, shape and size. The existing detection method like edge detection and object segmentation is difficult to recognize the objects when it comes to complex industrial environment. In the complex...
Motivated by product detection in supermarkets, this paper studies the problem of object proposal generation in supermarket images and other natural images. We argue that estimation of object scales in images is helpful for generating object proposals, especially for supermarket images where object scales are usually within a small range. Therefore, we propose to estimate object scales of images before...
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,...
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