This paper presents a range-image-based LiDAR point cloud upsampling method, utilizing the State Space Model (SSM) framework. The proposed method significantly reduces computational demands while maintaining high performance, improving efficiency in LiDAR point cloud upsampling tasks. We validated the effectiveness of our approach through experiments on the KITTI dataset. Results demonstrate that the SSM-based LiDAR upsampling method achieves a 69% reduction in FLOPs and a 72% decrease in number of parameters compared to the Swin Transformer model. This efficient solution offers enhanced LiDAR point cloud resolution, making it a promising approach for cost-effective applications in autonomous driving and related fields.

Abstract This study proposes a stack framework of light gradient boosting machine (LGBM) for Taiwan stock market index prediction. Stock market predictions have been regarded as a challenging task, as the market is affected by several factors such as political events, general economic conditions, institutional investors' choices, movement of the global market, psychology of investors. We construct a rich feature set to capture the impacts of global markets, institutional investors' choices, and the psychology of investors. A feature selection algorithm is proposed to choose important feature subset and enhance the training performance. To further improve the prediction accuracy, we employ stacking strategy to combine multiple classifiers together. A 10‐year period of the Taiwan stock exchange capitalization weighted stock index (TAIEX) is used to verify the performance of the proposed model. The experimental results suggest that our prediction model as well as the feature selection method can achieve good prediction performance.

Spatio-temporal databases aim at appropriately managing moving objects so as to support various types of queries. While much research has been conducted on developing query processing techniques, less effort has been made to address the issue of when and how to update location information of moving objects. Previous work shifts the workload of processing updates to each object which usually has limited CPU and battery capacities. This results in a tremendous processing overhead for each moving object. In this paper, we develop a novel update strategy, namely the possible region update strategy (PRUS), to explicitly indicate a time point at which object needs to update location information. As each object knows in advance when to update (meaning that it does not have to continuously check), the processing overhead can be greatly reduced. In addition, a possible region update procedure (PRUP) is designed to efficiently process the updates issued from moving objects. Extensive experiments are conducted to demonstrate the effectiveness and the efficiency of the proposed PRUS and PRUP.

This paper presents the design of Pool, an efficient and scalable data storage scheme for supporting multidimensional queries. The foundation of the work that makes the Pool approach superior in executing multi-dimensional queries is that it provides a novel and elegant higher dimension to two-dimensional data mapping mechanism. Our performance study proves the efficiency of the design.

Location dependent data (LDD) are those closedly related to specific locations. Services involving LDD are becoming more and more popular in recent years. To provide a high quality of service for these applications, prefetching LDD before a mobile client reaches the LDD site is an important factor to consider. In this paper, we analyze this problem and propose a benefit-oriented method for cost-effective prefetch of LDD. Our performance study shows that the method is superior to other traditional methods.

We refer "time-constrained services” to those requests that have to be replied to within a certain client-expected time duration. If the answer cannot reach the client within this expected time, the value of the information may seriously degrade or even become useless. On-demand channels may not be able to handle all time-constrained services without degrading the performance. How to handle these services in broadcast channels becomes crucial to balance the load of wireless systems. In this paper, we study this problem and find the minimum number of broadcast channels required for such a task. Also, we propose solutions for this problem when the available channels are insufficient. Our performance result reveals that only a moderate number of channels is required to promote these time-constrained services.

Data broadcasting is an efficient and highly scalable technique for delivering data to mobile clients in wireless environments. In this paper, we study the problem of scheduling broadcast data that are with an expected time within which the client is expecting to receive the data item. We analyze the problem and derive the minimum number of broadcast channels required for such a task. Also, we discuss the problems when the number of available channels is not enough. We propose novel solutions for both of the cases and the performance study indicates that our method is much better than the previous ones and performs very close to optimal.