Identification of Weather Conditions Related to Roadside LiDAR Data

Traffic data collection is essential for traffic safety and operations studies and has been recognized as a fundamental component in the development of intelligent transportation systems. In recent years, growing interest is shown by both industrial and academic communities in high-resolution data that can portray traffic operations for all transportation participants such as connected or conventional vehicles, transit buses, and pedestrians. Roadside Light Detection and Ranging (LiDAR) sensors can be deployed to collect such high-resolution traffic data sets. However, LiDAR sensing could be negatively affected in the context of rain, snow, and wind conditions as the collected 3D point clouds of surrounding objects may drift. Weather-caused impacts can lead to difficulties in data processing and even accuracy compromise. Consequently, solutions are desired and sought, focused on the issue that the affected data have been identified through a labor-intensive and time-consuming process. In this research, a methodology is proposed for developing an automatic identification of the LiDAR data sets that are affected by rain, snow, and wind conditions. First, the impacts of rain, snow, and wind are characterized using statistical measures. Detection distance offset (DDO) and Detection distance offset for wind (DDOW) are calculated and investigated, and it shows that rain or snow conditions can be differentiated according to the standard deviation of the DDOs. Snow conditions can be additionally identified using the sum of the DDOs. Unlike rain and snow, wind conditions can be recognized by the differences between the upper and lower boundaries of DDOs, and therefore, a separate analysis is developed. Based upon the multiple analyses developed in the research, an automatic identification process is designed. The thresholds for identifying rain, snow, and wind conditions are set up, respectively. The process is validated using realistic roadside LiDAR data collected at the intersection of McCarran Blvd and Evans Ave in Reno, Nevada. The validation demonstrated that the proposed identification could precisely detect affected data sets in the context of rain, snow, and wind conditions

Assessing Chinese Students’ Writing Performance in an American University: The Relationship between Selected Written Errors, Teacher’s Feedback, and Learners’ Interlanguage Experiences

The study examined Chinese students’ writing performance through the lens of corrective feedback and learners’ interlanguage experiences. It concludes that coding on paper may work only on learners who pay much attention to teachers’ feedback. It is always the work of both students and teachers to improve the accuracy in English writing

Target detection and classification using seismic signal processing in unattended ground sensor systems

This thesis studies the problem of target detection and classification in Unat-tended Ground Sensor (UGS) systems. One of the most challenging problems faced by target identification process is the design of a robust feature vector which is sta-ble and specific to a certain type of vehicle. UGS systems have been used to detect and classify a variety of vehicles. In these systems, acoustic and seismic signals are the most popularly used resources. This thesis studies recent development of target detection and classification techniques using seismic signals. Based on these studies, a new feature extraction algorithm. Spectral Statistics and Wavelet Coef-ficients Characterization (SSWCC), is proposed. This algorithm obtains a robust feature vector extracted from the spectrum, the power spectral density (BSD) and the wavelet coefficients of the signals. Shape statistics is used in both spectral and PSD analysis. These features not only describe the frequency distribution in the spectrum and PSD, but also shows the closeness of the magnitude of spectrum to the normal distribution. Furthermore, the wavelet coefficients are calculated to present the signal in the time-frequency domain. The energy and the distribution of the wavelet coefficients are used in feature extraction as well. After the features are obtained, principal component analysis (PGA) is used to reduce the dimension of the features and optimize the feature vector. Minimum-distance classifier and k-nearest neighbor (kNN) classifier are used to carry out the classification. Experimental results show that SSWCC provides a robust feature set for target identification. The overall performance level can reach as high as 90%

The Analysis of Chinese Convertible Bond Market

Convertible bond is a type of hybrid security with both bond- and stock-like features. The Chinese market of convertible bonds has developed dramatically during the last decade. This paper will conduct a comprehensive analysis of this market. Firstly, a brief introduction of convertible bond and the historical evolution of this market in China is presented, then we analyze various investment risks related to convertible bonds. Next, this paper proposes the basic valuation model for convertible bonds, which is the Black-Scholes model and modifies it by taking the delusion effect of conversion into account, leading to the Gailai-Schneller model. In addition, the differences of the outcomes obtained by these two models are compared and analyzed based on the pricing of Shanghai Electric convertible bond. In the sixth part, this paper mainly explains two types of applications of convertible bonds in portfolio management. In the end, several problems existing in Chinese convertible market as well as some suggestions for solving them are discussed