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Avionic system developers are currently working on innovative technologies that are required in view of the rapid expansion of global air transport and growing concerns for environmental sustainability of aviation sector. Novel Communication, Navigation and Surveillance (CNS) system designs are being developed in the CNS/Air Traffic Management (CNS/ATM) and Avionics (CNS+A) context for mission-and safety-critical applications. The introduction of dedicated software modules in Next Generation Flight Management Systems (NG-FMS), which are the primary providers of automated navigation and guidance services in manned aircraft and Remotely-Piloted Aircraft Systems (RPAS), has the potential to enable the significant advances brought in by time and trajectory based operations. High-integrity, high-reliability and all-weather services are required in the context of four dimensional Trajectory Based Operations / Intent Based Operations (TBO/IBO).

In this paper, a comparison has been carried out between two Formula 1 engine architectures: a traditional V12 and a 12 cylinder with three banks and one crankshaft, which will be referred to from here on as W12. This comparison is made in terms of geometrical features, as well as in terms of safety coefficients, torsional stiffness, state of balance and friction losses. The W12's crankshaft is 158 mm shorter and stiffer than the V12's. Furthermore, this crankshaft is simpler and lighter. The W12 engine front section is wider. The crankshaft of the W12 has a minimum safety factor that is 30% lower than the V12's under the same operating conditions (18000 rpm, bmep=13 bar). While the V12 is perfectly self-balanced, the secondary forces are out of balance in the W12's crankshaft. This unbalance is, however, no more critical than the one occurring in a V10 or V8. Friction losses in the W12 should be slightly lower in comparison to the V12.

To meet the requirements in relation to pollutants, CO2-emissions, performances, comfort and costs for 2025 timeframe, many technology options for the powertrain, that plays a key role in the vehicle, are possible. Beside the central aspect of reducing standard cycle consumption levels and emissions, consumer demands are also growing with respect to comfort and functionality. In addition, there is also the challenge of finding cost efficient ways of integrating technologies into a broad range of vehicles with different levels of hybridization. High degrees of electrification simultaneously provide opportunities to reduce the technology content of the internal combustion engines (ICE), resulting in a cost balancing compromise between combustion engine and hybrid technology. The design and optimization of powertrain topologies, functionalities, and components require a complex development process.

The paper deals with the reduction of pollutant emissions in urban areas by considering a Zero-Emission Zone (ZEZ) in which hybrid electric vehicles (HEVs) are allowed to be driven without using the internal combustion engine, as several cities have planned to realize in the next decades. Moreover, since vehicle connectivity has spread more and more in the last years, a vehicle-to-network (V2N) communication system has been taken into account to retrieve real-time navigation data from a map service provider and thus reconstructing the so-called electronic horizon, which is a reconstruction of the future conditions of the vehicle on the road ahead. The speed profile and the road slope are used as input for an on-board predictive control strategy of a plug-in HEV (PHEV). In particular, a dedicated algorithm predicts the amount of necessary energy to complete the city event in full-electric mode, giving a state of charge (SoC) target value.

Global Navigation Satellite Systems (GNSS) can support the development of low-cost and high performance navigation and guidance architectures for Unmanned Aircraft Systems (UAS) and, in conjunction with suitable data link technologies, the provision of Automated Dependent Surveillance (ADS) functionalities for cooperative Sense-and-Avoid (SAA). In non-cooperative SAA, the adoption of GNSS can also provide the key positioning and, in some cases, attitude data (using multiple antennas) required for automated collision avoidance. A key limitation of GNSS for both cooperative (ADS) and non-cooperative applications is represented by the achievable levels of integrity. Therefore, an Avionics Based Integrity Augmentation (ABIA) solution is proposed to support the development of an Integrity-Augmented SAA (IAS) architecture suitable for both cooperative and non-cooperative scenarios.

Multi-Sensor Data Fusion (MSDF) techniques involving satellite and inertial-based sensors are widely adopted to improve the navigation solution of a number of mission- and safety-critical tasks. Such integrated Navigation and Guidance Systems (NGS) currently do not meet the required level of performance in all flight phases of small Remotely Piloted Aircraft Systems (RPAS). In this paper an innovative Square Root-Unscented Kalman Filter (SR-UKF) based NGS is presented and compared with a conventional UKF governed design. The presented system architectures adopt state-of-the-art information fusion approach based on a number of low-cost sensors including; Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Navigation (VBN) sensors.

Accurate and robust tracking of objects is of growing interest amongst the computer vision scientific community. The ability of a multi-sensor system to detect and track objects, and accurately predict their future trajectory is critical in the context of mission- and safety-critical applications. Remotely Piloted Aircraft System (RPAS) are currently not equipped to routinely access all classes of airspace since certified Detect-and-Avoid (DAA) systems are yet to be developed. Such capabilities can be achieved by incorporating both cooperative and non-cooperative DAA functions, as well as providing enhanced communications, navigation and surveillance (CNS) services. DAA is highly dependent on the performance of CNS systems for Detection, Tacking and avoiding (DTA) tasks and maneuvers.