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Abstract Lane detection is an important component of advanced driving aided system (ADAS). It is a combined component of the planning and control algorithms. Therefore, it has high standards for the detection accuracy and speed. Recently several researchers have worked extensively on this topic. An increasing number of researchers have been interested in self-attention-based lane detection. In difficult situations such as shadows, bright lights, and nights extracting global information is effective. Regardless of channel or spatial attention, it cannot independently extract all global information until a complicated model is used. Furthermore, it affects the run-time. However trading in this contradiction is challenging. In this study, a new lane identification model that combines channel and spatial self-attention was developed. Conv1d and Conv2d were introduced to extract the global information. The model is lightweight and efficient avoiding difficult model calculations and massive matrices, In particular obstacles can be overcome under certain difficult conditions. We used the Tusimple and CULane datasets as verification standards. The accuracy of the Tusimple benchmark was the highest at 95.49%. In the CULane dataset, the proposed model achieved 75.32% in F1, which is the highest result, particularly in difficult scenarios. For the Tusimple and CULane datasets, the proposed model achieved the best performance in terms of accuracy and speed.

In this study, the Doppler migration technology in 5G mobile communication network was investigated as a vehicle-to-vehicle communication system. The advantages of using Doppler migration technology to investigate the anti-collision warning system are that it can detect a dozen vehicles in front of the vehicle equipped with it, and it is not disturbed by environmental factors such as light, rain, smoke, and dust. This system was used in a non-line-of-sight collision avoidance warning system in an observer vehicle to effectively carry out early warning judgments on multiple vehicles ahead of the observer vehicle. Judgments on traffic collision warnings were based on Doppler migration technology that signals between different vehicles to predict whether the observer vehicle will collide with a vehicle ahead of it. MATLAB software was used, and a crossroads was selected as the scenario for the simulation. To process the simulation, the starting coordinates, starting speed, acceleration, driving direction, and simulation times of each vehicle were input as the simulation parameters. All moving conditions of each vehicle in the simulation process were generated, including the coordinates, distance, speed, and angle between the observer vehicle and other vehicles. A flowchart for the process of making a judgment on driving collision warnings was proposed to judge if a collision between two vehicles would occur. [ABSTRACT FROM AUTHOR]

Taiwan currently faces an aging population and a rise in mortality rate caused by cardiovascular disease. Therefore, the prevention of cardiovascular disease and the constant tracking of physiological information are crucial. Photoplethysmography (PPG) is used to instantaneously measure and monitor a person's physiological information. Compared with electrocardiography, PPG measurements are more convenient and less expensive. However, PPG is more susceptible to the effects of motion artifacts. To accurately analyze the physiological information contained in PPG signals, the negative effects of motion artifacts on PPG signals must first be eliminated. In this study, independent component analysis was employed to separate independent components (i.e., PPG signals and noise) from raw signals with motion artifacts. Subsequently, independent components containing PPG signals were selected and the locations of the PPG signal components in the frequency spectrum were analyzed. Next, the raw signals were passed through a multi-bandpass filter specifically designed for this study to eliminate motion artifacts. Motion artifacts were created for an experiment using two types of finger movements: vertical and horizontal finger movements. From the independent component analysis of the independent components, the waveforms of the filtered PPG signals in the time and frequency domains were acquired, which were then compared with the results of heartbeat measurement. [ABSTRACT FROM AUTHOR]

In this paper, we consider the implementation of different Zadoff-Chu sequences to different modulation levels in communication systems with multiple modulation levels. The base station of the system can dynamically estimate the number of users in each modulation level by detecting the Zadoff-Chu sequences it receives. The base station then calculates the throughput of each modulation level and determines if any modulation level is saturated. If a modulation level is saturated, then the base station implements an additional mechanism on the users in the modulation level with the largest throughput. This mechanism makes users enter a waiting period before allowing further data transmissions so as to mitigate or reduce the base station load to accomplish the tasks and functions of the self-organizing network. [ABSTRACT FROM AUTHOR]