By measuring and processing scattered Global Navigation Satellite System (GNSS) signals from the Earth, the GNSS reflectometry (GNSS-R) technique has shown the potential in remote sensing applications including altimetry, ocean wind retrieval, and soil moisture measurement. The Taiwanese TRITON satellite mission carries a GNSS-R payload to collect reflected/scattered GNSS signals. The satellite was launched in October, 2023. The paper presents preliminary results of the GNSS-R payload, The function and performance of the payload are verified and the delay-Doppler maps generated by the GNSS-R payload can be utilized for remote sensing applications. Based on the results, the development of the future GNSS-R/RO (reflectometry/radio occultation) payload is discussed in a hope to facilitate GNSS-based remote sensing data. In this endeavor, the potential of polarimetric GNSS is taken into account.

A digital twin is often adopted in computer simulations to expedite autonomous vehicle developments by using the simulated 3D environment that reflects a physical environment. In particular, traffic simulations are a crucial part of training the driving logic before the field test of an autonomous vehicle is performed on specific regions to adapt to the region-specific, dynamic traffic conditions. Currently, the traffic conditions are either synthesised by tools (e.g. using mathematical models) or created manually (using domain knowledge), which cannot reflect the realistic, region-specific conditions or will require extensive labour works. In this article, we propose an automatic methodology to model the real-world traffic conditions captured by the sensor data and to reproduce the modeled traffic in the digital twin. We have built the tools based on the methodology and use the KITTI dataset to validate the effectiveness of the tools. To recreate the region-specific traffic, we present the results of capturing, modelling, and recreating the two-wheeler traffic condition on the Southeast Asia road. Our experimental results show that the proposed method facilitates the simulation of real-world, Southeast Asia-specific traffic conditions by removing the needs of the synthesised traffic and the labour hours.

The omnipresence Global Navigation Satellite System (GNSS) signals have not only provided high accuracy navigation but also facilitated many remote sensing missions. In a GNSS reflectometry (GNSS-R) mission, the reflected signals are processed to form delay Doppler maps (DDMs) so that the properties including roughness, ocean wind speed, and soil moisture can be retrieved. The paper describes the design, status, and special features of an upcoming spaceborne GNSS-R mission, FORMOSAT-7R.

This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.

Location techniques are crucial in many engineering and medical applications. The paper attempts to assess sensor network localization techniques in an indoor environment based on range measurements. As the range measurements are often subject to errors due to sensor and environment. Different methods may result in different positioning performance. In the paper, several localization techniques are simulated and implemented in an experiment setup to assess the resulting accuracy and implementation complexity.

This paper devises a freeway controller that is capable of stabilizing traffic flow when the traffic system is in the unstable (congested) phase, in which a shock wave is likely to occur in the presence of any inhomogeneity and where the system is on the verge of a jam condition. Two types of traffic controllers are developed through the use of either a speed command approach that can be implemented in an intelligent transportation system (ITS) or ramp metering that is a typical way of preventing a freeway from overloading. By means of the feedback linearization technique, the discretized macroscopic traffic flow model is reformulated, in which the desired change of volume in each section is treated as a virtual input. By exploring the casual relations among density, speed, and flow change, the corresponding speed commands can be determined. The traffic flow control problem is formulated as an Hinfincontrol design problem so that uncertainties that are associated with the macroscopic model can be taken into account. Simulations show that the devised controller can effectively stabilize the traffic flow in the unstable phase. Design flexibilities associated with the method are also discussed.

In the paper, a new application and development of a highly integrated GPS receiver with reflected GPS signals for Ground Object Detection and Digital Terrain Elevation Mapping will be described. First of all, both RHCP and LHCP antennas are employed so that direct and reflected signals can be acquired simultaneously. The direction of arrival of the signals may be along the reflected signal path or even along the line-of-sight of a particular satellite. The goal of the study is to exploit the carrier phase, reflectivity of Ll/L2 SNR components of the reflected signals and direct signals for stream fresh water, ocean water and ground object detection with surface. The Sea states are predicted by using Doppler shifts due to surface reflection as a moving surface. An integer ambiguity resolution algorithm has also been implemented. During the development and test stage, the digital terrain elevation data and satellite’s images has been used and mapped with the integrated software.

This paper aims at devising a longitudinal vehicle controller to avoid collisions in a platoon of vehicles with explicit consideration of brake input limits. A new spacing policy is conceived for eliminating the dependence of the parameter of spacing on traveling velocity. It is shown that the proposed policy yields better traffic throughput and that the corresponding margin of traffic flow stability is extended in comparison to constant time-headway policy. An adaptive controller based on a semi-autonomous scheme is devised to bear the system uncertainties and to have more mobility to respond any individual emergency that may arise. The parameter design of the spacing policy is provided so as to account for any possible emergent stopping scenarios and for individual brake limitations.

The paper addresses a decentralized control problem of traffic flow. Most existing freeway traffic flow control relies on centralized scheme to adjust the control variable in order to enhance efficiency and avoid congestions. The paper devises a cellular framework and decentralized control scheme for traffic flow control so that the control scheme does not rely on global information. The proposed control scheme exploits neighboring cellular information and sliding mode control technique to prevent the buildup of traffic flow and aggravating propagation of shock waves. The proposed traffic flow controller is simulated to show its effectiveness in attenuating shock waves and fluctuations.

In this paper, the tracking problem is considered for robot manipulators in the presence of static friction, bounded disturbance, and modeling uncertainties. A composite tracking control strategy is proposed in which an adaptive friction estimation is used to estimate the extent of friction and a robust controller is then designed to enhance the overall stability and robustness. Performance issues of robust adaptive friction control are illustrated in simulation made for a two degree of freedom planar robot manipulator.

Traffic flow models are used to characterize traffic behavior or to describe fundamental diagram (flow-density relationship) of traffic conditions. From the viewpoint of static traffic characteristic, these models mainly provide information of traffic states (free flow or congestion) and trend of traffic variation in a highway section or intersection. However, dynamic properties observed in short time interval reveal that hysteresis phenomenon are likely to occur during traffic state-transitions. The hysteresis phenomenon is visible in the transition paths both in the flow-density and velocity-density diagrams. The hysteresis is either due to drivers' asymmetrical desired control speed in anticipation and relaxation modes or on traffic conditions of demand and supply in upstream and downstream. The hysteresis transition shows how the traffic quality evolves during minutes to hours. It is thus expected that a better traffic flow modeling and control can be achieved if hysteresis transition can be correctly modeled and used in traffic prediction. The paper develops a generalized mathematical hysteresis model based on Duhem operator. The proposed hysteresis model can describe hysteresis phenomenon in each state to state transition. An identification process is also applied to make our model more flexible in different traffic conditions. Besides modeling, issue related to flow stage transitions and congested trend can be predicted or estimated, and, accordingly, some traffic control strategies can also be devised. Simulation results are provided to illustrate transitions between free flow states and congested states. The simulation results also show that our model can represent the hysteresis phenomenon in different state transitions. The research is expected to shed light on intelligent transportation systems.

The paper applies several concepts in robust control research such as linear matrix inequalities, edge theorem, parameter-dependent Lyapunov function, and Popov criteria to investigate the stability property of Hopfield-type neural networks. The existence and uniqueness of an equilibrium is formulated as a matrix determinant problem. An induction scheme is used to find the equilibrium. To verify whether the determinant is nozero for a class of matrix, a numerical range test is proposed. Several robust control techniques in particular linear matrix inequalities are used to characterize the local stability of the neural networks around the equilibrium. The global stability of the Hopfield neural networks is then addressed using a parameter-dependent Lyapunov function technique. All these results are shown to generalize existing results in verifying the existence/uniqueness of the equilibrium and local/global stability of Hopfield-type neural networks.