Identifying driving heterogeneity plays an important role in improving traffic safety and efficiency. This paper proposes a novel framework to identify driving heterogeneity from the underlying characteristics of driving behaviour. The framework includes three processes: Action phase extraction, Action pattern calibration, and Action pattern classification. The concepts of Action phase and Action patterns are proposed to decipher and interpret driving behaviours. Action phases are extracted by rule-based segmentation methods and Action patterns are calibrated based on an unsupervised learning approach. The extraction and calibration processes provide a rigorous labelling approach for the attention-based LSTM Action pattern classification process. Evaluation of the framework on a large-scale naturalistic driving dataset reveals six distinct Action patterns. The implementation of the attention mechanism to LSTM models significantly enhanced both the accuracy and time efficiency of Action pattern identification. The proposed framework offers benefits in detecting and reducing variability in driving behaviour through ITS applications such as user-based traffic management, personalised Advanced Driver Assistance Systems (ADAS), and advanced autonomous vehicles (AV) design, thereby enhancing road safety and traffic efficiency.

Presents corrections to the paper, (Corrections to “A Novel Framework for Identifying Driving Heterogeneity Through Action Patterns”).

This paper deals with the integrated signal design to simultaneously achieve a desired radar beampattern and multi-users communication for a dual-function Multiple Input Multiple Output (MIMO) system. Inter-pulse modulation strategy via jointing the information embedding subpulses positions selections and differential phase modulation is proposed for communication function, while beampattern Integrated Sidelobe Level (ISL) is minimized to enhance radar detectability. Meanwhile, constant envelope, communication modulation and mainlobe width constraints are forced. To tackle the resulting nonconvex and NP-hard optimization problem, the Linearized Approximation Alternating Direction Penalty Method (LA-ADPM) is proposed. Finally, numerical results demonstrate the designed integrated signal is capable of realizing high data rate communication and ensuring acceptable radar performance.

Ophthalmologists have used fundus images to screen and diagnose eye diseases. However, different equipments and ophthalmologists pose large variations to the quality of fundus images. Low-quality (LQ) degraded fundus images easily lead to uncertainty in clinical screening and generally increase the risk of misdiagnosis. Thus, real fundus image restoration is worth studying. Unfortunately, real clinical benchmark has not been explored for this task so far. In this paper, we investigate the real clinical fundus image restoration problem. Firstly, We establish a clinical dataset, Real Fundus (RF), including 120 low- and high-quality (HQ) image pairs. Then we propose a novel Transformer-based Generative Adversarial Network (RFormer) to restore the real degradation of clinical fundus images. The key component in our network is the Window-based Self-Attention Block (WSAB) which captures non-local self-similarity and long-range dependencies. To produce more visually pleasant results, a Transformer-based discriminator is introduced. Extensive experiments on our clinical benchmark show that the proposed RFormer significantly outperforms the state-of-the-art (SOTA) methods. In addition, experiments of downstream tasks such as vessel segmentation and optic disc/cup detection demonstrate that our proposed RFormer benefits clinical fundus image analysis and applications.

As an alternative to conventional FIR filters, IIR filter architecture based on orthonormal Laguerre functions is proposed for applications in narrow-band filtering. A Laguerre IIR filter design methodology is presented via a frequency transformation in the digital domain. The Laguerre filter can then be efficiently designed using a min-max criterion via a modified Parks-McClellan (1972) FIR filter design algorithm. The proposed Laguerre IIR filter architecture is easy to implement because it needs only a small number of components and therefore well suited for VLSI circuits. The impact of the selection of filter pole on the filter stability and phase linearity is discussed in the paper. The robustness issue of the narrow-band Laguerre IIR filter is also addressed.

As an alternative to conventional FIR filters, an optimal IIR filter architecture based on orthonormal Laguerre functions is proposed for Sigma-Delta (/spl Sigma/-/spl Delta/) demodulators. A Laguerre IIR filter design methodology is presented via the optimization of a quadratic function subject to a linear and a quadratic constraint. An efficient procedure to perform the optimization is discussed. The proposed IIR filter is easy to implement because it needs only a small number of components and therefore well suited for VLSI implementations. The impact of the selection of filter pole on the filter stability and phase linearity is discussed. The robustness issue of the Laguerre IIR filter is also addressed.