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With more and more devices being introduced into vehicles to provide additional driving functionality, driving experience is seemingly getting more complex and frustrated due to an influx of individual messages. To help manage the information, individuals need a mechanism to orchestrate the operations of the in-vehicle devices so that such frustration can be eased. In this study, the authors propose an in-vehicle communication scheme for multi-system integration, where an effective system service discovery protocol is used. This proposed design also contains mutual communication methods and the data exchange methodologies for system integration. Experimental results with performance evaluation demonstrate the success of the proposed design.

Instead of finding clusters in the full feature space, subspace clustering is an emergent task which aims at detecting clusters embedded in subspaces. Most of previous works in the literature are density-based approaches, where a cluster is regarded as a high-density region in a subspace. However, the identification of dense regions in previous works lacks of considering a critical problem, called "the density divergence problemrdquo in this paper, which refers to the phenomenon that the region densities vary in different subspace cardinalities. Without considering this problem, previous works utilize a density threshold to discover the dense regions in all subspaces, which incurs the serious loss of clustering accuracy (either recall or precision of the resulting clusters) in different subspace cardinalities. To tackle the density divergence problem, in this paper, we devise a novel subspace clustering model to discover the clusters based on the relative region densities in the subspaces, where the clusters are regarded as regions whose densities are relatively high as compared to the region densities in a subspace. Based on this idea, different density thresholds are adaptively determined to discover the clusters in different subspace cardinalities. Due to the infeasibility of applying previous techniques in this novel clustering model, we also devise an innovative algorithm, referred to as DENCOS (density conscious subspace clustering), to adopt a divide-and-conquer scheme to efficiently discover clusters satisfying different density thresholds in different subspace cardinalities. As validated by our extensive experiments on various data sets, DENCOS can discover the clusters in all subspaces with high quality, and the efficiency of DENCOS outperformes previous works.

In this paper, we first study an important but unsolved dilemma in the literature of subspace clustering, which is referred to as ldquoinformation overlapping-data coveragerdquo challenge. Current solutions of subspace clustering usually invoke a grid-based apriori-like procedure to identify dense regions and construct subspace clusters afterward. Due to the nature of monotonicity property in apriori-like procedures, it is inherent that if a region is identified as dense, all its projected regions are also identified as dense, causing overlapping/redundant clustering information to be inevitably reported to users when generating clusters from such highly correlated regions. However, naive methods to filter redundant clusters will incur a challenging problem in the other side of the dilemma, called the ldquodata coveragerdquo issue. Note that two clusters may have highly correlated dense regions but their data members could be highly different to each other. Arbitrarily removing one of them may lose the coverage of data with clustering information, thus likely reporting an incomplete and biased clustering result. In this paper, therefore, we further propose an innovative algorithm, called "nonredundant subspace cluster miningrdquo (NORSC), to efficiently discover a succinct collection of subspace clusters while also maintaining the required degree of data coverage. NORSC not only avoids generating the redundant clusters with most of the contained data covered by higher dimensional clusters to resolve the information overlapping problem but also limits the information loss to cope with the data coverage problem. As shown by our experimental results, NORSC is very effective in identifying a concise and small set of subspace clusters, while incurring time complexity in orders of magnitude better than that of previous works.

With more and more devices being introduced into vehicles to provide additional driving functionality, driving experience is seemingly getting more complex and frustrated due to an influx of individual messages. To help manage the information, individuals need a mechanism to orchestrate the operations of the in-vehicle devices so that such frustration can be eased. In this study, the authors propose an in-vehicle communication scheme for multi-system integration, where an effective system service discovery protocol is used. This proposed design also contains mutual communication methods and the data exchange methodologies for system integration. Experimental results with performance evaluation demonstrate the success of the proposed design.

With more and more devices being introduced into vehicles to provide additional driving functionality, driving experience is seemingly getting more complex and frustrated due to an influx of individual messages. To help manage the information, we need a mechanism to orchestrate the operations of the in-vehicle devices so that such frustration can be eased. In this study, we propose an in-vehicle communication scheme for multi-system integration, where an effective system service discovery protocol is used. This proposed design also contains mutual communication methods and the data exchange methodologies for system integration. Experimental results with data analysis demonstrate the success of the proposed design.

Aiming to provide the wireless communications in some critical situations such as the data exchange between vehicle to vehicle and vehicle to infrastructure, some dedicated short range communications(DSRC) have been developed, one of those technologies named Wireless Access in Vehicular Environments(WAVE) is a suite of wireless technology IEEE 1609-family based on the standard IEEE 802.11p, which is mainly developed for avoiding the vehicle crashes and traffic accidents. In this article, we propose a mechanism based on the DSRC/WAVE and some applications based on it to deliver the traffic information so that the drivers can receive the adequate information to drive smoothly and safely.

Recent studies show that intersections account for around 43% of all traffic accidents and enhanced communication between traffic signals and vehicles can possibly lead to a 50% reduction in such accidents. With the advancement of dedicated short range communications (DSRC) technologies such as IEEE 1609 (Wireless Access in Vehicular Environments, WAVE), it has started to bring such communication solutions to reality; however, to cope with inherent complexity at intersections, more factors need to be considered to complete the chosen solutions. In this paper, we propose an advanced driving-assistance system (ADAS) which utilizes a dual-mode communication mechanism to provide data at a greater range than DSRC-only systems, thereby achieving significant performance improvement in Signal Phase and Timing (SPaT) applications. When vehicles approach an intersection, they are individually recommended to travel at a safe speed, which not only increases driving safety, but also results in a smooth traffic flow with lower fuel consumption.

To enhance the success rate of merger and acquisition, the acquiring party should make due diligence investigation into the target enterprise's counting materials and financial situations when filtering the target enterprises. Meanwhile, the acquiring party must thoroughly analyze the target enterprise's motive for selling, making sure that all the financial resources of the target enterprise well satisfy its acquisition criteria. Thus, the acquiring party can determine the most appropriate target enterprise. This thesis tends to define the financial features of the target enterprise and the primary factors that affect the filtration of target enterprise in order to provide suggestions for the acquiring party to filter the target enterprise that satisfy its acquisition criteria.

In this paper, a problem, called "the density divergence problem" is explored. This problem is related to the phenomenon that the densities of the clusters vary in different subspace cardinalities. We take the densities into consideration in subspace clustering and explore an algorithm to adaptively determine different density thresholds to discover clusters in different subspace cardinalities.

In this paper, we explore a new problem of ”temporal dense region query” to discover the dense regions in the constrainted time intervals which can be separated or not. A Querying tEmporal Dense Region framework (abbreviated as QED) proposed to deal with this problem consists of two phases: (1) an offline maintaining phase, to maintain the statistics of data by constructing a number of summarized structures, RF-trees; (2) an online query processing phase, to provide an efficient algorithm to execute queries on the RF-trees. The QED framework has the advantage that by using the summarized structures, RF-trees, the queries can be executed efficiently without accessing the raw data. In addition, a number of RF-trees can be merged with one another efficiently such that the queries will be executed efficiently on the combined RF-tree. As validated by our empirical studies, the QED framework performs very efficiently while producing the results of high quality.