6 research outputs found
Routing in Mobile Ad-Hoc Networks using Social Tie Strengths and Mobility Plans
We consider the problem of routing in a mobile ad-hoc network (MANET) for
which the planned mobilities of the nodes are partially known a priori and the
nodes travel in groups. This situation arises commonly in military and
emergency response scenarios. Optimal routes are computed using the most
reliable path principle in which the negative logarithm of a node pair's
adjacency probability is used as a link weight metric. This probability is
estimated using the mobility plan as well as dynamic information captured by
table exchanges, including a measure of the social tie strength between nodes.
The latter information is useful when nodes deviate from their plans or when
the plans are inaccurate. We compare the proposed routing algorithm with the
commonly-used optimized link state routing (OLSR) protocol in ns-3 simulations.
As the OLSR protocol does not exploit the mobility plans, it relies on link
state determination which suffers with increasing mobility. Our simulations
show considerably better throughput performance with the proposed approach as
compared with OLSR at the expense of increased overhead. However, in the
high-throughput regime, the proposed approach outperforms OLSR in terms of both
throughput and overhead
Automated Music Success Prediction
We investigate the uses and limitations of MFCC analysis for feature extraction from music files in the domain of genre recognition. Intra-genre and Inter-genre classification is explored. We implement a method of genre classification based on MFCC extraction, K-means clustering, and KNN analysis. We demonstrate the efficacy of our method through testing, yielding a 99% accuracy rate.
Memory-Based Learning for Visual Odometry
Abstract — We present and examine a technique for estimating the ego-motion of a mobile robot using memory-based learning and a monocular camera. Unlike other approaches that rely heavily on camera calibration and geometry to compute trajectory, our method learns a mapping from sparse optical flow to platform velocity and turn rate. We also demonstrate an efficient method of computing high-quality sparse optical flow, and techniques for using this sparse optical flow as input to a supervised learning method. We employ a voting scheme of many learners that use subsets of the sparse optical flow to cope with variable dimensionality and reduce the dimensionality of each learner. Finally, we perform experiments in which we examine the learned mapping for visual odometry, investigate the effects of varying the reduced dimensionality of the sparse optical flow state, and quantify the accuracy of two variations of our learner scheme. Our results indicate that our learning scheme estimates monocular visual odometry mainly from points on the ground plane, and reflect to a degree the minimum dimensionality imposed by the problem. In addition, we show that while this memory-based learning method cannot yet estimate ego-motion as accurately as recent geometric methods, it is possible to learn, with no explicit model of camera calibration or scene structure, complicated mappings that take advantage of properties of the camera and the environment. I