There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Computer Controlled Systems: Theory and Design

Over the past decade we have witnessed the evolution of wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of real-world mobile sensor applications that require position estimation.

/pdf/a-survey-on-localization-for-mobile-wireless-sensor-networks-dnh83kqrru.pdf

A survey on localization for mobile wireless sensor networks

Modern hardware and software development has led to an evolution of user interfaces from command-line to natural user interfaces for virtual immersive environments. Gestures imitating real-world interaction tasks increasingly replace classical two-dimensional interfaces based on Windows/Icons/Menus/Pointers (WIMP) or touch metaphors. Thus, the purpose of this paper is to survey the state-of-the-art Human-Computer Interaction (HCI) techniques with a focus on the special field of three-dimensional interaction. This includes an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition. Focus is on interfaces based on the Leap Motion Controller (LMC) and corresponding methods of gesture design and recognition. Further, a review of evaluation methods for the proposed natural user interfaces is given.

Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

Human facial expressions change with different states of health; therefore, a facial-expression recognition system can be beneficial to a healthcare framework. In this paper, a facial-expression recognition system is proposed to improve the service of the healthcare in a smart city. The proposed system applies a bandlet transform to a face image to extract sub-bands. Then, a weighted, center-symmetric local binary pattern is applied to each sub-band block by block. The CS-LBP histograms of the blocks are concatenated to produce a feature vector of the face image. An optional feature-selection technique selects the most dominant features, which are then fed into two classifiers: a Gaussian mixture model and a support vector machine. The scores of these classifiers are fused by weight to produce a confidence score, which is used to make decisions about the facial expression’s type. Several experiments are performed using a large set of data to validate the proposed system. Experimental results show that the proposed system can recognize facial expressions with 99.95% accuracy.

A Facial-Expression Monitoring System for Improved Healthcare in Smart Cities

Triangulation with active beacons is widely used in the absolute localization of mobile robots. The geometric triangulation algorithm allows the self-localization of a robot on a plane. However, the three beacons it uses must be "properly ordered" and the algorithm works consistently only when these beacons within the triangle form the robot. This paper presents an improved version of the algorithm, which does not require beacon ordering and works over the whole navigation plane except for a few well-determined lines where localization is not possible.

Generalized geometric triangulation algorithm for mobile robot absolute self-localization

https://repositorium.sdum.uminho.pt/bitstream/1822/6057/1/An%20Improved%20Version%20of%20the%20Generalized%20Geometric%20Triangulation%20Algorithm.pdf

An improved version of the Generalized geometric triangulation algorithm

Individuals with Autism Spectrum Disorder (ASD) typically present difficulties in engaging and interacting with their peers. Thus, researchers have been developing different technological solutions as support tools for children with ASD. Social robots, one example of these technological solutions, are often unaware of their game partners, preventing the automatic adaptation of their behavior to the user. Information that can be used to enrich this interaction and, consequently, adapt the system behavior is the recognition of different actions of the user by using RGB cameras or/and depth sensors. The present work proposes a method to automatically detect in real-time typical and stereotypical actions of children with ASD by using the Intel RealSense and the Nuitrack SDK to detect and extract the user joint coordinates. The pipeline starts by mapping the temporal and spatial joints dynamics onto a color image-based representation. Usually, the position of the joints in the final image is clustered into groups. In order to verify if the sequence of the joints in the final image representation can influence the model's performance, two main experiments were conducted where in the first, the order of the grouped joints in the sequence was changed, and in the second, the joints were randomly ordered. In each experiment, statistical methods were used in the analysis. Based on the experiments conducted, it was found statistically significant differences concerning the joints sequence in the image, indicating that the order of the joints might impact the model's performance. The final model, a Convolutional Neural Network (CNN), trained on the different actions (typical and stereotypical), was used to classify the different patterns of behavior, achieving a mean accuracy of 92.4% ± 0.0% on the test data. The entire pipeline ran on average at 31 FPS.

https://repositorium.sdum.uminho.pt/bitstream/1822/74307/1/sensors-21-04342.pdf

Skeleton Driven Action Recognition Using an Image-Based Spatial-Temporal Representation and Convolution Neural Network.

The face embodies a large portion of the human emotionally expressive behaviour. Moreover, facial expressions are used to display emotional states and to manage interactions, being one of the most important channels of non-verbal communication. Although the process of recognition and displaying emotions is an easy task for the majority of humans, it is a very difficult task for individuals with Autism Spectrum Disorders (ASD). The present paper is a summary of a work developed under a Master Thesis on Industrial Electronics Engineering and Computers from the University of Minho, Portugal. The main goal of the work was the development and application of interactive and assistive technologies to support and promote new adaptive teaching/learning approaches for children with ASD. Therefore, it was proposed a system that uses the recent Intel RealSense 3D sensor to promote imitation and recognition of facial expressions, using a RoboKind Zeno R50 robot (ZECA) as a mediator in social activities. The system was first validated in the research laboratory and then tested in school environment with typically developing children and children with ASD.

/pdf/mirroring-and-recognizing-emotions-through-facial-3a7gedn4i7.pdf

Mirroring and recognizing emotions through facial expressions for a RoboKind platform

Systems and devices that can recognize human affects have been in development for a considerable time. Facial features are usually extracted by using video data or a Microsoft Kinect sensor. The present paper proposes an emotion recognition system that uses the recent Intel RealSense 3D sensor, whose reliability and validity in the field of emotion recognition has not yet been studied. This preliminary work focus on happiness and sadness. The system extracts the user’s facial Action Units and head motion data. Then, it uses a Support Vector Machine to automatically classify the emotion expressed by the user. The results point out the adequacy of Intel RealSense for facial features extraction in emotion recognition systems as well as the importance of determining head motion when recognizing sadness.

João Sena Esteves

Papers

Generalized geometric triangulation algorithm for mobile robot absolute self-localization

An improved version of the Generalized geometric triangulation algorithm

Skeleton Driven Action Recognition Using an Image-Based Spatial-Temporal Representation and Convolution Neural Network.

Mirroring and recognizing emotions through facial expressions for a RoboKind platform

Happiness and sadness recognition system: preliminary results with an Intel RealSense 3D Sensor