This paper proposes an absolute phase retrieval method for complex object measurement based on quantized phase-coding and connected region labeling. A specific code sequence is embedded into quantized phase of three coded fringes. Connected regions of different codes are labeled and assigned with 3-digit-codes combining the current period and its neighbors. Wrapped phase, more than 36 periods, can be restored with reference to the code sequence. Experimental results verify the capability of the proposed method to measure multiple isolated objects.

Unmanned aerial vehicles (UAVs) can be used to relay sensing information and computational workloads from ground users (GUs) to a remote base station (RBS) for further processing. In this paper, we employ multiple UAVs to assist with the collection of sensing information in a terrestrial wireless sensor network. All of the information collected by the UAVs can be forwarded to the RBS. We aim to improve the energy efficiency for sensing-data collection and transmission by optimizing UAV trajectory, scheduling, and access-control strategies. Considering a time-slotted frame structure, UAV flight, sensing, and information-forwarding sub-slots are confined to each time slot. This motivates the trade-off study between UAV access-control and trajectory planning. More sensing data in one time slot will take up more UAV buffer space and require a longer transmission time for information forwarding. We solve this problem by a multi-agent deep reinforcement learning approach that takes into consideration a dynamic network environment with uncertain information about the GU spatial distribution and traffic demands. We further devise a hierarchical learning framework with reduced action and state spaces to improve the learning efficiency by exploiting the distributed structure of the UAV-assisted wireless sensor network. Simulation results show that UAV trajectory planning with access control can significantly improve UAV energy efficiency. The hierarchical learning method is more stable in learning and can also achieve higher sensing performance.

To face the challenges of fossil fuel shortage and stringent emission norms, there is growing interest in the potential usage of alternative fuels such as bio-ethanol and bio-butanol in internal combustion engines. More recently, Acetone–Butanol–Ethanol (ABE), the intermediate product of bio-butanol fermentation, has been gaining a lot of attention as an alternative fuel. The literature shows that the acetone in the ABE blends plays an important part in improving the combustion performance and emissions, owing to its higher volatility. Acetone and ethanol are the low-value byproducts during bio-butanol production, so using acetone and ethanol as fuel additives may have both economic and environmental benefits. This study focuses on the differences in combustion, performance and emission characteristics of a port-injection spark-ignition engine fueled with pure gasoline (G100), ethanol-containing gasoline (E10 and E30) and acetone-ethanol-gasoline blends (AE10 and AE30 at A:E volumetric ratio of 3:1). The tests were conducted at 1200 RPM, under gasoline maximum brake torque (MBT) at 3 bar and 5 bar brake mean effective pressure (BMEP). Performance and emission data were measured under various equivalence ratios. Based on the comparison of combustion phasing, brake thermal efficiency, brake specific fuel consumption and various emissions of different fuels, it was found that using acetone as an oxygenate additive with the default ECU calibration (for gasoline) maintained the thermal efficiency and showed lower unburned HC emissions.

The accurate air-fuel ratio (AFR) control is crucial for the exhaust emission reduction based on the three-way catalytic converter in the spark ignition (SI) engine. The difficulties in transient cylinder air mass flow measurement, the existing fuel mass wall-wetting phenomenon, and the unfixed AFR path dynamic variations make the design of the AFR controller a challenging task. In this paper, an adaptive AFR regulation controller is designed using the feedforward and feedback control scheme based on the dynamical modelling of the AFR path. The generalized predictive control method is proposed to solve the problems of inherent nonlinearities, time delays, parameter variations, and uncertainties in the AFR closed loop. The simulation analysis is investigated for the effectiveness of noise suppression, online prediction, and self-correction on the SI engine system. Moreover, the experimental verification shows an acceptable performance of the designed controller and the potential usage of the generalized predictive control in AFR regulation application.

Air-fuel ratio (AFR) control is important for the exhaust emission reduction while using the three-way catalytic converter in the spark ignition (SI) engine. However, the transient cylinder air mass is unable to acquire by sensors directly and it may limit the accuracy of AFR control. The complex engine dynamics and working conditions make the intake air estimation a challenge work. In this paper, a novelty design of intake air observer is investigated for the port-injected SI engine. The intake air dynamical modeling and the parameter fitting have been carried out in detail. Extended Kalman Filter (EKF) has been used to optimize the instantaneous cylinder charge estimation and minimize the effort of pump gas fluctuation, random noise, and measurement noise. The experiment validation has been conducted to verify the effectiveness of the proposed method.