Abstract
A highly sensitive D-shaped optical fiber refractive index sensor based on surface plasmon resonance is designed and analyzed by using numerical simulations based on the finite element method. The flat surface of the fiber is coated with a gold layer that works as the plasmon active metal, followed by a titanium oxide (TiO2) layer, which is employed to enhance the performance of the sensor. The results demonstrate that the proposed sensor's properties highly depend on the metal and dielectric coating's thickness, enabling the tuning of the resonance wavelength. By supposing the system noise to be 0.1 nm, the theoretical maximum sensitivity was found to be 30000 nm/RIU, with a resolution of 3.33x10(-6) RIU and a figure of merit (FOM) of 312.46 RIU-1, for an analyte with a refractive index of 1.41. The sensor's sensitivity and FOM is improved upon the state of the art, possibly opening new windows of study in the fields of biological and chemical sensing.

Abstract
The electric spring (ES) is a novel voltage compensator which is series with a non-critical load to regulate the critical load voltage. The voltage fluctuation is caused by wind speed fluctuation, load fluctuation, and generator tripping. In busbar voltage drop situation, the electric spring decreases the voltage of non-critical load in order to support the critical load (busbar) voltage. All the non-critical loads couldn't work under any voltage (for example 0.5 pu). In this paper, a control strategy founded on active and reactive power compensations has been proposed for voltage control of critical loads on a reference value while it controls the voltage of non-critical loads between an acceptable boundary. The proposed controller has two voltage control loops which adjusts active and reactive power of the electric spring. The experimental results from the case study show that the ES with the proposed control strategy can effectively mitigate double voltage control of both critical and non-critical loads while dynamically managing the demand response of the system at the same time.

Abstract
This paper presents a speed control of the reluctance machine for electric drive applications with fast dynamic demand. To get high-performance speed control, a cascade control algorithm is developed based on linear control technique. The controller is designed using the Root Locus Methodology and implemented on a numerical simulation platform. The design using Root Locus Methodology proved to be a viable approach and showed that various problems associated with the structural torque ripple of the electric motor can be solved. An important aspect of this work is the role played by model linearization in testing the sensitivity of the controller performance to specific parameter changes. The controller is applied to a simulated non-linear switched reluctance motor model in order to evaluate their performances. Simulation results showed that high-performance control for Switched Reluctance Motor has been achieved.

Abstract
Time series data is composed of observations of one or more variables along a time period. By analyzing the variability of the variables we can reveal patterns that repeat or that are correlated, which helps to understand the behaviour of the variables over time. Our method finds frequent distributions of a target variable in time series data and discovers relationships between frequent distributions in consecutive time intervals. The frequent distributions are found using a new method, and relationships between them are found using association rules mining.

Abstract
Formal modeling and automatic analysis are essential to achieve a trustworthy software design prior to its implementation. Alloy and its Analyzer are a popular language and tool for this task. Frequently, rather than a single software artifact, the goal is to develop a full software product line (SPL) with many variants supporting different features. Ideally, software design languages and tools should provide support for analyzing all such variants (e.g., by helping pinpoint combinations of features that could break a property), but that is not currently the case. Even when developing a single artifact, support for multi-variant analysis is desirable to explore design alternatives. Several techniques have been proposed to simplify the implementation of SPLs. One such technique is to use background colors to identify the fragments of code associated with each feature. In this paper we propose to use that same technique for formal design, showing how to add support for features and background colors to Alloy and its Analyzer, thus easing the analysis of software design variants. Some illustrative examples and evaluation results are presented, showing the benefits and efficiency of the implemented technique.

Abstract
Autonomous Unmanned Aerial Vehicles (UAVs) become an important field of research in which multiple applications can be designed, such as surveillance, deliveries, and others. Thus, studies aiming to improve the performance of these vehicles are being proposed: from new sensing solutions to more robust control techniques. Additionally, the autonomous UAV has challenges in flight stages as the landing. This procedure needs to be performed safely with a reduced error margin in static and dynamic targets. To solve this imperative issue, many applications with computer vision and control theory have been developed. Therefore, this paper presents an alternative method to train a multilayer perceptron neural network based on fuzzy Mamdani logic to control the landing of a UAV on an artificial marker. The advantage of this method is the reduction in computational complexity while maintaining the characteristics and intelligence of the fuzzy logic controller. Results are presented with simulation and real tests for static and dynamic landing spots. For the real experiments, a quadcopter with an onboard computer and ROS is used.