Wi-Fi Received Signal Strength Indicator (RSSI) has been considered as one of most important resources for indoor localization. However, the conventional RSSI fingerprint method needs recording many ground-truth locations. In this paper, we propose an unsupervised Wi-Fi RSSI learning without true location. Pseudo-labels are made by another machine learning model, which uses inertial measurement unit (IMU) as training input. From the experiments in an office building, the proposed algorithm validates to learn an unsupervised Wi-Fi RSSI positioning model by resulting in 0.8 CDF at 2 meter distance.

The event-triggered model predictive control (MPC) reduces energy consumption for updating control sequences while maintaining the originality of the MPC, which copes with hard constraints on dynamical systems. In the presence of large uncertainties, however, the standard event-triggered MPC may generate too frequent event occurrences. To compensate for unknown uncertainties, this paper applies a statistical learning to event-triggered MPC. The stability and the feasibility of the proposed control system are analyzed in regard to the statistical learning influences, such as the number of training samples, model complexity, and learning parameters. Accordingly, the event-triggering policy is established to guarantee the stability. We evaluate the proposed algorithm for the tracking problem of a nonholonomic model perturbed by uncertainties. In comparison with the standard event-triggered control scheme, the simulation results of the proposed method show better tracking performance with less frequent event triggers.

This study addresses an operation of unmanned aerial vehicles (UAVs) in a network environment where there is time-varying network delay. The network delay entails undesirable effects on the stability of the UAV control system due to delayed state feedback and outdated control input. Although several networked control algorithms have been proposed to deal with the network delay, most existing studies have assumed that the plant dynamics is known and simple, or the network delay is constant. These assumptions are improper to multirotor-type UAVs because of their nonlinearity and time-sensitive characteristics. To deal with these problems, we propose a networked control system using model predictive control (MPC) designed under the consideration of multirotor characteristics. We also apply a Gaussian process (GP) to learn an unknown nonlinear model, which increases the accuracy of path planning and state estimation. Flight experiments show that the proposed algorithm successfully compensates the network delay and Gaussian process learning improves the UAV's path tracking performance.

This paper presents a new design of multirotor, named as `Tilting Thruster Type' (T3)-multirotor. The new platform is equipped with mechanically separated thrusters, which can take any fuselage posture within a specified range regardless of any direction of translational acceleration. A specially designed servo-linkage mechanism is employed for relative attitude control between the thruster and the fuselage. Mathematical modeling and analysis of the new platform are conducted to explore the control method of the dynamically complex system. For demonstrating the potential of the new T3-multirotor, an autonomous level flight is performed where the fuselage maintains zero roll and pitch angle during the entire flight. Both simulation and experimental results are provided with detailed analysis.

This paper proposes a new semi-supervised machine learning for localization. It improves localization efficiency by reducing efforts needed to calibrate labeled training data by using unlabeled data, where training data come from received signal strengths of a wireless communication link. The main idea is to treat training data as spatio-temporal data. We compare the proposed algorithm with the state-of-art semi-supervised learning methods. The algorithms are evaluated for estimating the unknown location of a smartphone mobile robot. The experimental results show that the developed learning algorithm is the most accurate and robust to the varying amount of training data, without sacrificing the computation speed.

This paper suggests a mapless indoor localization using wifi received signal strength (RSS) of a smartphone, collected by multiple people. A new trajectory learning algorithm by combining a dynamic time warping and a machine learning technique is proposed in order to generate an alternative map. Moreover, we combine particle filter and Gaussian process (GP) for the position estimation, because it can use the alternative map as the probabilistic function (the prior), and can use probabilistic relationship (the likelihood) between wifi RSSs and location. Field experimental results confirm the usefulness of our algorithm when the map is not available and robustness against outliers, in that the accuracy of the proposed localization is similar to that using the true map information.

As people around the world are spending increasing amounts of time online, the question of how online experiences are linked to health and well-being is essential. This paper presents how activities on Facebook are associated with the depressive states of users. Based on online logs of 212 young adults, we show not only the sheer size of the network but also the frequency and diversity of interactions on social networks have close associations with depression. Depressed individuals reported smaller involved networks regarding comments and likes, the two popular forms of interactions. In contrast to the decreased level of interactions, depressed individuals showed an increase in the wall post rates and were active online during midday, which can be interpreted as an endemic behavior linked to the perceived degree of loneliness among young adults who are avid users of social media. We discuss these findings from theoretical, empirical, and subjective perspectives.

Online least square support vector regression (LS-SVR) is an extension from a standard SVR for fast learning. In this paper, we consider a combination of the online LS-SVR and semi-supervised learning in order to boost estimation accuracy. The semi-supervised learning is useful for real-time and complex estimation applications because it uses a small amount of the labeled data but sufficient unlabeled data that can be easily obtained. The algorithms are evaluated for two experiments, i.e. state estimation of a robot arm and forecasting of chaotic time-series. The experimental results show that the proposed algorithm yields more accurate estimation than the compared online LS-SVR.

This paper presents the design principles, implementation, and evaluation of the RETOS operating system which is specifically developed for micro sensor nodes. RETOS has four distinct objectives, which are to provide (1) a multithreaded programming interface, (2) system resiliency, (3) kernel extensibility with dynamic reconfiguration, and (4) WSN-oriented network abstraction. RETOS is a multithreaded operating system, hence it provides the commonly used thread model of programming interface to developers. We have used various implementation techniques to optimize the performance and resource usage of multithreading. RETOS also provides software solutions to separate kernel from user applications, and supports their robust execution on MMU-less hardware. The RETOS kernel can be dynamically reconfigured, via loadable kernel framework, so a application-optimized and resource-efficient kernel is constructed. Finally, the networking architecture in RETOS is designed with a layering concept to provide WSN-specific network abstraction. RETOS currently supports Atmel ATmega128, TI MSP430, and Chipcon CC2430 family of microcontrollers. Several real-world WSN applications are developed for RETOS and the overall evaluation of the systems is described in the paper.

This demonstration shows the programming development suite of the RETOS operating system for sensor networks, which provides a robust and multithreaded programming interface to application programmers. We first demonstrate how to build the RETOS kernel on the TI MSP430, Atmel ATmega 128 and Chipcon CC2430 family of microcontrollers. The application or a kernel module is then compiled and disseminated, via wireless channel, to the target motes. The GUI-based RMon network management tool for RETOS is also demonstrated to monitor the networked sensors, and even to control the system's parameters or applications running on them via a remote shell. The system is demonstrated to run on a mixed set of MSP430, ATmega128 and CC2430-based motes. Overall, our demonstration will convince attendees of the programming convenience of developing sensor network applications using RETOS, which is, indeed, a mature and practical system that can be used to develop real-world applications.

Compared to a centralized system for acoustic source localization using WSN, a distributed system has many advantages in terms of scalability, power consumption, and response time. Complexity is an important factor to consider when implementing a distributed system because of the limited resource of WSN devices. The complexity of the source localization algorithm is related to the area required for location estimation. Since reducing the size of the estimation area influences the accuracy of the source localization system, a policy should be carefully designed to reduce the size. In this paper, the authors use a Voronoi diagram to define an estimation area. The Voronoi diagram-based mechanism may yield an optimized estimation area and accuracy, but creating a Voronoi diagram is not suitable for WSN devices due to the high implementation overhead. This paper proposes an efficient mechanism to create the estimation area, which can be implemented in WSN devices and achieve reasonable accuracy, compared to the ideal Voronoi diagram-based one. The proposed algorithm has been applied to a distributed source localization algorithm, and compared with a Voronoi diagram through extensive simulations.