Methods of improving the coordinates determining accuracy using inertial geodetic systems

The authors consider the principle of geodetic definitions using inertial systems in the absence of signals from satellite radio navigation systems and insufficient density of the initial geodetic base. A technique of improving the coordinates’ accuracy determining using inertial geodetic systems is proposed. Compensation of these systems’ errors is performed through joint equalization of data received from the inertial system, gyrotheodolite and rangefinder. In order to test the methodology, a mathematical model of the ground object movement with an inertial geodetic system was compiled. The simulation results confirm the suitability of the developed methodology. A full-scale experiment was conducted to test the efficiency of the technique. Its results are consistent with those of the simulation. The analysis of the data obtained enables concluding that the developed technique provides an increase in the accuracy of determining coordinates using inertial geodetic systems in the absence or distortion of signals from satellite radio navigation systems and insufficient density of the initial geodetic base. That is why it can be used in operational geodetic training under conditions of autonomy.

FaceSense

Face touch is an unconscious human habit. Frequent touching of sensitive/mucosal facial zones (eyes, nose, and mouth) increases health risks by passing pathogens into the body and spreading diseases. Furthermore, accurate monitoring of face touch is critical for behavioral intervention. Existing monitoring systems only capture objects approaching the face, rather than detecting actual touches. As such, these systems are prone to false positives upon hand or object movement in proximity to one's face (e.g., picking up a phone). We present FaceSense, an ear-worn system capable of identifying actual touches and differentiating them between sensitive/mucosal areas from other facial areas. Following a multimodal approach, FaceSense integrates low-resolution thermal images and physiological signals. Thermal sensors sense the thermal infrared signal emitted by an approaching hand, while physiological sensors monitor impedance changes caused by skin deformation during a touch. Processed thermal and physiological signals are fed into a deep learning model (TouchNet) to detect touches and identify the facial zone of the touch. We fabricated prototypes using off-the-shelf hardware and conducted experiments with 14 participants while they perform various daily activities (e.g., drinking, talking). Results show a macro-F1-score of 83.4% for touch detection with leave-one-user-out cross-validation and a macro-F1-score of 90.1% for touch zone identification with a personalized model.

The Interaction between Motion and Texture in the Sense of Touch

Besides providing information on elementary properties of objects-like texture, roughness, and softness-the sense of touch is also important in building a representation of object movement, and the movement of our hands. Neural and behavioral studies shed light on the mechanisms and limits of our sense of touch in the perception of texture and motion, and of its role in the control of movement of our hands. The interplay between the geometrical and mechanical properties of the touched objects, such as shape and texture, the movement of the hand exploring the object, and the motion felt by touch, will be discussed in this article. Interestingly, the interaction between motion and textures can generate perceptual illusions in touch. For example, the orientation and the spacing of the texture elements on a static surface induces the illusion of surface motion when we move our hand on it or can elicit the perception of a curved trajectory during sliding, straight hand movements. In this work we present a multiperspective view that encompasses both the perceptual and the motor aspects, as well as the response of peripheral and central nerve structures, to analyze and better understand the complex mechanisms underpinning the tactile representation of texture and motion. Such a better understanding of the spatiotemporal features of the tactile stimulus can reveal novel transdisciplinary applications in neuroscience and haptics.

Semantic Reconstruction of Vocabulary with a Speed Value Derived from Verbs of Object Movement

The article deals with the verbs of moving an object by the subject, developing the semantics of low and high speed of action. The research is mainly based on the vocabulary of Russian folk dialects, but the facts of the literary language are also involved. Several lexical groups are distinguished, within which “speed” meanings can develop, depending on the way of movement indicated by the verbs: without lifting off the ground; by the strength of your movement; by air; appropriation; giving away; putting into something. Within each group, the main models of the development of lexemes with the semantics of speed, determined by the features of the called action, the meaning of affixes and possible associative series are described. It is found that, although the movement of an object by a subject is a fairly extensive lexical group, a limited set of models are presented directly in the designation of speed, which are implemented using synonymous verbs (tyanut’ [pull], tashchit’ [drag] and volochit’ [crawl along]; brosat’ [throw], kidat’ [cast] etc; khvatat’ [grab] etc.). The correlations of the semantics of speed with other semantic fields that arise within the framework of this thematic group are described.

Object Detection and Movement Tracking Using Tubelets and Faster RCNN Algorithm with Anchor Generation

Object detection in images and videos has become an important task in computer vision. It has been a challenging task due to misclassification and localization errors. The proposed approach explored the feasibility of automated detection and tracking of elephant intrusion along forest border areas. Due to an alarming increase in crop damages resulted from movements of elephant herds, combined with high risk of elephant extinction due to human activities, this paper looked into an efficient solution through elephant’s tracking. The convolutional neural network with transfer learning is used as the model for object classification and feature extraction. A new tracking system using automated tubelet generation and anchor generation methods in combination with faster RCNN was developed and tested on 5,482 video sequences. Real-time video taken for analysis consisted of heavily occluded objects such as trees and animals. Tubelet generated from each video sequence with intersection over union (IoU) thresholds have been effective in tracking the elephant object movement in the forest areas. The proposed work has been compared with other state-of-the-art techniques, namely, faster RCNN, YOLO v3, and HyperNet. Experimental results on the real-time dataset show that the proposed work achieves an improved performance of 73.9% in detecting and tracking of objects, which outperformed the existing approaches.

Combining Sensors Information to Enhance Pneumatic Grippers Performance

The gripper is the far end of a robotic arm. It is responsible for the contacts between the robot itself and all the items present in a work space, or even in a social space. Therefore, to provide grippers with intelligent behaviors is fundamental, especially when the robot has to interact with human beings. As shown in this article, we built an instrumented pneumatic gripper prototype that relies on different sensors’ information. Thanks to such information, the gripper prototype was able to detect the position of a given object in order to grasp it, to safely keep it between its fingers and to avoid slipping in the case of any object movement, even very small. The gripper performance was evaluated by means of a generic grasping algorithm for robotic grippers, implemented in the form of a state machine. Several slip tests were carried out on the pneumatic gripper, which showed a very fast response time and high reliability. Objects of various size, shape and hardness were employed to reproduce different grasping scenarios. We demonstrate that, through the use of force, torque, center of pressure and proximity information, the behavior of the developed pneumatic gripper prototype outperforms the one of the traditional pneumatic gripping devices.

Effects of an Intervention for Promoting Basic Motor Competencies in Middle Childhood

The development of motor competencies is necessary for participation in the culture of sport, exercise, and physical activity, which in turn supports the development of a healthy lifestyle. A lack of physical activity in childhood and deficits in motor performance emphasize the relevance of interventions for promoting basic motor competencies. However, there are research desiderata with regard to such interventions. This article describes an intervention program for promoting basic motor competencies in middle childhood (around 6 to 10 years of age). The intervention was investigated in a longitudinal study from June 2019 to January 2020 (n = 200; 58% girls, M = 8.84 years, SD = 0.63) at three primary schools. The intervention was conducted once a week in physical education (PE). The comparison group participated in regular PE. The intervention showed significant effects on basic motor competencies in object movement but not in self-movement. The results demonstrate that positive effects on basic motor competencies can be achieved with the help of a relatively simple intervention. Further longitudinal studies are desirable as a means of substantiating the results and developing evidence-based concepts to support children in their development in the best possible way.

Sentences with Object-oriented Adverbials and the Syntax-Semantics Interface

SOOAs refer to the phenomenon that the state adjective in the adverbial position is semantically associated with the object. In this paper this fact will be accounted for by invoking the properties of a pre-syntactic level of semantic representation and its interplay with syntax proper. It will be argued that the object-oriented adverbial is not derived from the attributive; in effect, it is base-generated in the complement position. A model of projection of arguments that allows for this will be proposed. It will be shown that the other special properties of SOOAs follow from the way the verb’s object and complement are represented at the pre-syntactic level. In particular, it will be shown that the underlying structure must satisfy both the requirement of the syntactic system and the requirement of the semantic system. The presence of any symbol in a representation is conditional. The theta-roles of internal arguments are assigned by the predicate, which is locally constrained, whereas the theta-roles of external arguments are assigned by the maximal projection of the predicate, viz. VP. When an external argument occurs, there is an empty predicate position in the representation, for there is an asymmetry between the conceptual system and the syntactic system. Derivation involves Move-α and Generalized Transformation. Different use of derivation methods gives rise to various forms of constructions in Chinese. Similarly, different semantic orientations result from different distributions. The state adjective is base-generated in the position behind the object because its nature is to serve the function of the complement of the object. It co-occurs with the object in the embedded VP because they are closely related to each other in terms of semantics. There is no overt predicate between the object and the state adjective. The state adjective occurs in other positions, which is the result of movement. Movement falls into two types, viz. object movement and state adjective movement. Object movement is prior to state adjective movement. In order to satisfy the requirement of feature checking, the object moves to the position NP. Then the state adjective moves to the major predicate and merges with it so as to maintain its semantic association with the state adjective and to serve the function of the complement. It follows that the difference between Chinese SOOAs and English as well as German SOOAs lies in the distance of movement of state adjectives. In Chinese, there are causative markers, resultative markers, and manner markers that can license the state adjective in the landing sites and help to maintain its semantic association with the object while in English and German there are no such markers. As a consequence, the object in Chinese-type SOOAs does not move while the state adjective moves out of the embedded VP to the empty verb position where the manner marker is inserted. The major predicate moves to the same position and merges with the newly-formed syntactic object ADJ-MANN. Furthermore, Chinese-type SOOAs allow the state adjective to precede the major predicate as the manner marker can license the state adjective. In contrast, in English-type SOOAs, neither the state adjective nor the object moves. Moreover, English-type SOOAs do not allow the state adjective to come before the major predicate because there is no manner marker to license it.