Abstract
Developing ground robots for crop monitoring and harvesting in steep slope vineyards is a complex challenge due to two main reasons: harsh condition of the terrain and unstable localization accuracy obtained with Global Navigation Satellite System. In this context, a reliable localization system requires an accurate and redundant information to Global Navigation Satellite System and wheel odometry based system. To pursue this goal we benchmark 3 well known Visual Odometry methods with 2 datasets. Two of these are feature-based Visual Odometry algorithms: Libviso2 and SVO 2.0. The third is an appearance-based Visual Odometry algorithm called DSO. In monocular Visual Odometry, two main problems appear: pure rotations and scale estimation. In this paper, we focus on the first issue. To do so, we propose a Kalman Filter to fuse a single gyroscope with the output pose of monocular Visual Odometry, while estimating gyroscope's bias continuously. In this approach we propose a non-linear noise variation that ensures that bias estimation is not affected by Visual Odometry resultant rotations. We compare and discuss the three unchanged methods and the three methods with the proposed additional Kalman Filter. For tests, two public datasets are used: the Kitti dataset and another built in-house. Results show that our additional Kalman Filter highly improves Visual Odometry performance in rotation movements.

Abstract
Although the quantum theory of the optical response of individual atoms to coherent light with frequencies close to electronic transitions and the fluid equations for a gas are well known and understood from first principles, they are developed independently of each other and therefore cannot be applied directly to describe many of the quantum collective and transport phenomena that occur in cold atomic gases, especially in what regards their interaction with optical pulses and beams. Few attempts have been made to derive a consistent formalism and theory that are capable to model this type of systems, and those which exist rely on the adaptation of several ad-hoc hypothesis and simplifications, such as space and time dependent density operators. In this paper we provide the theoretical foundations and establish a formalism capable of paving the way for the development of new simulation tools and to explore new problems in nonlinear optics out of equilibrium.

Abstract
In this paper, we show how stream fusion, a program transformation technique used in functional programming, can be adapted for an Object-Oriented setting. This makes it possible to have more Stream operators than the ones currently provided by the Java Stream API. The addition of more operators allows for a greater deal of expressiveness. To this extent, we show how these operators are incorporated in the stream setting. Furthermore, we also demonstrate how a specific set of optimizations eliminates overheads and produces equivalent code in the form of for loops. In this way, programmers are relieved from the burden of writing code in such a cumbersome style, thus allowing for a more declarative and intuitive programming approach.

Abstract
An overview of the work under development within the EU-funded collaborative project MAESTRI is presented in this chapter. The project provides a framework of new Industrial methodology, integrating several tools and methods, to help industries facing the fourth industrial revolution. This concept, called the MAESTRI Total Efficiency Framework (MTEF), aims to advance the sustainability of manufacturing and process industries by providing a management system in the form of a flexible and scalable platform and methodology. The MTEF is based on four pillars: a) an effective management system targeted at continuous process improvement; b) Efficiency assessment tools to support improvements, optimization strategies and decision-making support; c) Industrial Symbiosis paradigm to gain value from waste and energy exchange; d) an Internet-of-Things infrastructure to support easy integration and data exchange among shop-floor, business systems and MAESTRI tools.

Abstract
We show a deterministic simulation (or lifting) theorem for composed problems f o Eq where the inner function (the gadget) is Equality on n bits. When f is a total function on p bits, it is easy to show via a rank argument that the communication complexity of f o Eq is Q(deg(f) " n). However, there is a surprising counter -example of a partial function f on p bits, such that any completion f' of f has deg(f)= Q(p), and yet f o Eq has communication complexity 0(n). Nonetheless, we are able to show that the communication complexity of f o Eq is at least D(f) " n for a complexity measure D(f) which is closely related to the AND -query complexity of f and is lower -bounded by the logarithm of the leaf complexity of f. As a corollary, we also obtain lifting theorems for the set-disjointness gadget, and a lifting theorem in the context of parity decision -trees, for the NOR gadget. As an application, we prove a tight lower -bound for the deterministic communication complexity of the communication problem, where Alice and Bob are each given p -many n -bit strings, with the promise that either all of the strings are distinct, or all -but -one of the strings are distinct, and they wish to know which is the case. We show that the complexity of this problem is e(p " n).

Abstract
Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price-elastic demand curve that relies on several factors such as estimation of a customer's elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price-elastic demand curve so that the DR providers apply their selected load profiles ranked in the high priority to the independent system operator (ISO). The energy and reserve markets clearing procedures have been run by using a multi-objective decision-making framework. In fact, its objective function includes the operation cost and the customer's disutility based on the final individual load profile for each DR provider. A two-stage stochastic model is implemented to solve this scheduling problem, which is a mixed-integer linear programming approach. The presented approach is tested on a modified IEEE 24-bus system. The performance of the proposed model is successfully evaluated from economic, technical and wind power integration aspects from the ISO viewpoint.