Abstract This paper deals with real-time obstacle detection and track- ing using multi-layer LIDAR data. We present two algorithms to cluster raw data coming from LIDAR sensors. The rst algorithm is based on a dynamic clustering approach while the second one relies on the con- nectivity between the laser impacts. Both algorithms take into account the inaccuracy and the uncertainty of... This paper deals with real-time obstacle detection and track- ing using multi-layer LIDAR data. We present two algorithms to cluster raw data coming from LIDAR sensors. The rst algorithm is based on a dynamic clustering approach while the second one relies on the con- nectivity between the laser impacts. Both algorithms take into account the inaccuracy and the uncertainty of the data sources. We propose a tracking approach based on the belief theory to estimate the dynamic state of the detected objects in order to predict their future maneuvers. The objects are then ltered using an intelligent ROI that depends on a dynamic evolution area computed from proprioceptive information of the ego-vehicle. We evaluate and validate the whole chained process on real data-sets. Read More Read Less

Abstract Today we are surrounding with large data related to student performance (class participation, attendance, pre student history, quiz result, subject dependency, student CGPA till to final semester). In this paper we will evaluate the reason of student failure basis on the previous data, predict the risk of failure for next course so that students may be mentally prepare for offered... Today we are surrounding with large data related to student performance (class participation, attendance, pre student history, quiz result, subject dependency, student CGPA till to final semester). In this paper we will evaluate the reason of student failure basis on the previous data, predict the risk of failure for next course so that students may be mentally prepare for offered course as well dependency level of the course. In engineering it is common practice if a student doesn’t knows about the basic course he/she can’t perform well in advance courses of same scopes. In this paper we will back trace the failure cause with the help of decision tree. This work wills also help out to estimate the risk in early phase, which can help the teachers to design an effective planning for the students who are at risk. We have used the six algorithms for prediction and risk analysis ID3 gives the best results as compared to other. In this paper we have used the data set of CEME, NUST. Our dataset consists of 450 records extracted from five degrees (DE_29, DE_30, DE_31, DE_32, and DE_33). Read More Read Less

Abstract While many dimensionality reduction algorithms have been proposed in recent years, most of them are designed for single view data and cannot cope with multi-view data directly. Dimensionality reduction algorithms in recent ten years, both in theory and application have great breakthrough. In the face of dozens, hundreds or even thousands of dimension by dimension reduction to the data... While many dimensionality reduction algorithms have been proposed in recent years, most of them are designed for single view data and cannot cope with multi-view data directly. Dimensionality reduction algorithms in recent ten years, both in theory and application have great breakthrough. In the face of dozens, hundreds or even thousands of dimension by dimension reduction to the data from high dimensional space to a low dimensional space and extract the essential characteristics of low dimensional data. In many real-world pattern applications such as face recognition, multiple feature descriptors can provide complementary information in characterizing image from different viewpoints. Motivated by this concern, we propose a new multi-view discriminative manifold embedding (MDME) method for classification by making use of intra-class geometry and inter-class marginal information as well as complementary information of multiple feature representations. Experimental results on face recognition demonstrate the effectiveness of the proposed algorithm. Read More Read Less