This paper deals with the design and development of the B2S4B platform that bridges Smart City ecosystems (the business world) and city sensors: the IoT and citizens (the IoT and the social worlds).This platform allows the reconfiguration of Smart City business processes (BPs) based on the detected events and knowledge derived over time.In support of this, the concept of Smart City Observers (SCO) was introduced which acts as a man-in-a-middle between BPs, and the IoT and the social worlds by defining what should be monitored and how the respective observations could impact these worlds.In its turn, B2S4B analyses the detected events from all active SCOs and based on the predefined knowledge allows the synchronized reconfiguration of a BP and of the city sensors that feed the SCOs with accurate information.This paper proposes an approach for manual and automatic adaptation of Smart City BPs based on events reported by SCOs.In order to demonstrate the usability and effectiveness of the proposed approach this paper illustrates how this platform could be used in a case study on Smart City traffic control.

Composite state convergence is a novel scheme applied for the bilateral control of a telerobotic system.The scheme offers an elegant design procedure and employs only three communication channels to establish synchronization between a single-master and a single-slave robotic system.This paper expands the capability of the composite state convergence scheme to accommodate any number of master and slave systems and proposes a disturbance observer-based composite state convergence architecture where k-master systems can cooperatively control l-slave systems in the presence of uncertainties.A systematic method is presented to compute the control gains while observer gains are determined in a standard way.To validate the proposed architecture, MATLAB simulations are performed on symmetric and asymmetric arrangements of single-degree-of-freedom teleoperation systems.Finally, experimental results are obtained using Quanser's Qube-Servo systems in QUARC/Simulink environment.

The outstanding performance of modern gas turbine power plants requires an efficient and robust control strategy in order to recover the losses caused by the undesirable effects and the instabilities related to their operation. Also, these losses can be the cause of their unstable dynamic behaviour during full-load and partial load operation in the unplanned shutdown state. Hence, choosing the right control strategy can improve the operation of this type of machine by increasing its efficiency by up to 60%. This paper proposes the implementation of an innovative control strategy based on a generalized predictive adaptive control algorithm for monitoring the rotation speed of a Solar Titan 130 gas turbine, with the purpose of considering the constraints related to the nonlinear behavior of a turbine during large variations in machine load. The aim is to automatically adjust the parameters of the regulator of the turbine's control loop in real time, with a recursive estimation of these parameters. Using the experimental input/output measurements in order to study its behavior, by integrating the predictive control estimators, ensures the superior performance of the analyzed gas turbine in terms of energy, efficiency and robustness regarding the parametric uncertainties and the variation of the turbine load.

By tradition, automation has been defined as a process that controls a number of functions or tasks without direct human intervention. In the mid '80s, some doubts about the opportunity and technical feasibility of full automation were expressed and influential scientists such as Peter Drucker and Umberto Eco viewed the computer as a "moron" and as a "stupid tool that functions only in the hands of intelligent people", respectively. Over the years, the technology has evolved and, consequently, the knowledge and skills of the people who use the computers and automation devices in their professional and private life have been continuously improved. At present, the meaning of automation together with the division of work between human and automation devices have dramatically changed and the early vision of Joseph Licklider about the "man-computer symbiosis" appears to be almost fully brought to life. The evolution is even more accelerated by the adaptation efforts that people make nowadays with a view to mitigating, to the extent possible, the unfavourable impact of the current pandemic on our lives and returning to a normal situation. The paper contains a brief historical account about how the computers and automation have enabled and stimulated new working styles and, at the same time, contributed to improving the quality of people's life.

An optimal selection of a WSN topology protocol and a suitable lightweight cipher algorithm is a highly important task, whose success ensures both the maximum utilization and the security of a wireless sensor network. This paper presents a method to determine the optimal combination of a WSN topology protocol and a lightweight cipher algorithm in the context of power consumption throughout an entire network. A selection method based on multi-attribute decision-making methods is proposed. These methods, used for the first time in a study of sensor networks, provide a ranking of the optimal combinations of topologies and encryption algorithms that meet the requirements of wireless sensor networks such as authentication, data integrity, reliability, availability, confidentiality, and energy efficiency.

In this paper, a Radial Basis Function Network (RBFN) controlled Sensorless Hybrid Electric Vehicle (HEV) using a Brushless DC Motor (BLDC) is presented.In this context, hybrid energy sources (Photovoltaic& Fuel cell) are designed for supplying electrical energy to the HEV.This involves the design of a Maximum Power Point Tracking (MPPT) system using a Fuzzy Logic controller (FLC) in order to track the maximum power obtained from hybrid energy sources under various weather conditions.The speed of HEV drives is efficiently controlled by using the RBFN-ANN controller in comparison with sensor based control techniques.The performance of the proposed HEV was tested for two types of controllers, namely the Proportional Integral (PI) and the RBFN-ANN controller.The performance of the system is analysed with respect to the three different load torque conditions.The performance comparison is made in terms of generated torque ripple and speed response.The proposed hybrid electric vehicle using a RBFN-ANN controller is validated through the MATLAB/Simulink environment.

Image matting is one of the most important tasks in the computer vision community whose popularity has increased in recent years. This is a highly critical method in video and image editing applications, which involves the separation of the foreground from the background of an image. The previous methods provide a low accuracy when the background and foreground of an image are similar. This paper proposes an effective matting method that integrates the combination of a supervised deep learning matting network generator and a self-supervised refinement network. The generator uses a supervised encoder-decoder network for the extraction of the foreground and alpha matte from the original input image. The results obtained by this network are employed by the self-supervised refinement network to evaluate the newly created composite images and ultimately improving the matting process. The proposed method has obtained better results in comparison with other methods, which makes it more reliable.

In this manuscript we intend to analyze how blockchain technology can improve supply chain variables and technology transfer in medium-sized enterprises from IT&C sector. Blockchain technology is expected to enhance the velocity and reliability of business and managerial processes and increase competitiveness for IT&C companies. We sought to see if these combined elements lead to the generation of competitive advantage, which in turn influences the firm performance. Based on the study of relevant literature, we developed a conceptual and methodological framework and test it on 383 medium-sized companies from IT&C sector, using the Confirmatory Factor Analysis. The results of the study show that blockchain technology positively influences supply chain variables and technology transfer, with direct effects on the firm performance

Composite state convergence scheme is a reduced-complexity version of the state convergence controller for teleoperation system. It employs a smaller number of control gains and communication channels used to synchronize the motion of a single master-slave system in a desired dynamic way. The present study aims at generalizing the composite state convergence scheme so that l-slave systems can follow the weighted motion of k-master systems. To achieve this, at first, composite variables of all master and slave systems are transmitted across the communication channel along with operators' forces and a set of k+l+2kl control gains is defined. In the second stage, the design procedure of the existing composite state convergence scheme is extended for multiple systems and the control gains are determined through the solution of coupled equations. Finally, to validate the findings, simulations and semi-real time experiments are performed in MATLAB/Simulink/QUARC environment by considering different configurations of teleoperation systems.