Learning the optimal synchronization rates in distributed SDN control architectures

Since the early development of Software-DefinedNetwork (SDN) technology, researchers have been concernedwith the idea of physical distribution of the control plane to ad-dress scalability and reliability challenges of centralized designs.However, having multiple controllers managing the networkwhile maintaining a “logically-centralized” network view bringsadditional challenges. One such challenge is how to coordinatethe management decisions made by the controllers which isusually achieved by disseminating synchronization messages ina peer-to-peer manner. While there exist many architecturesand protocols to ensure synchronized network views and drivecoordination among controllers, there is no systematic method-ology for deciding the optimal frequency (or rate) of messagedissemination. In this paper, we fill this gap by introducingthe SDN synchronization problem: how often to synchronize thenetwork views for each controller pair. We consider two differentobjectives; first, the maximization of the number of controllerpairs that are synchronized, and second, the maximization of theperformance of applications of interest which may be affectedby the synchronization rate. Using techniques from knapsackoptimization and learning theory, we derive algorithms withprovable performance guarantees for each objective. Evaluationresults demonstrate significant benefits over baseline schemes thatsynchronize all controller pairs at equal rate

Finger detection and hand posture recognition based on depth information

In this work, we propose a novel framework for automatic finger detection and hand posture recognition, based mainly on depth information. Our method locates apex-shaped structures in a hand contour and deals efficiently with the challenging problem of partially merged fingers. Hand posture recognition is achieved using Fourier Descriptors of the contour, while global information about the fingers helps reducing the size of the search space. Our experiments on a dataset obtained from a Kinect device confirm the high recognition accuracy of our approach. © 2014 IEEE

Low-Complexity Hand Gesture Recognition System for Continuous Streams of Digits and Letters

In this paper, we propose a complete gesture recognition framework based on maximum cosine similarity and fast nearest neighbor (NN) techniques, which offers high-recognition accuracy and great computational advantages for three fundamental problems of gesture recognition: 1) isolated recognition; 2) gesture verification; and 3) gesture spotting on continuous data streams. To support our arguments, we provide a thorough evaluation on three large publicly available databases, examining various scenarios, such as noisy environments, limited number of training examples, and time delay in system's response. Our experimental results suggest that this simple NN-based approach is quite accurate for trajectory classification of digits and letters and could become a promising approach for implementations on low-power embedded systems. © 2013 IEEE

Initialization of dynamic time warping using tree-based fast Nearest Neighbor

An efficient way to perform Dynamic Time Warping (DTW) search is by using the LBKeogh lower bound, which can eliminate a large number of candidate vectors out of the search process. Although effective, LBKeogh begins the DTW search using the first candidate vector, which is typically arbitrarily chosen. In this work, we propose initializing the LBKeogh-based DTW search using the Euclidean Distance Nearest Neighbor, derived by a fast tree-based Nearest Neighbor technique. Our experimental results suggest that, on one hand, this simple NN-based approach is quite accurate for trajectory classification of digit and letter gesturing and can initialize the DTW search very efficiently, thus requiring about 20% less search time than existing DTW implementations without any drop in recognition accuracy. © 2016 Elsevier B.V. All rights reserved

Full action instances for motion analysis

Motion analysis is an important component of surveillance,video annotation and many other applications. Current work focuses on the tracking of moving entities, the representation of their actions and the classification of sequences. A wide range of methods are available for the characterization and analysis of human activity. This work presents an original approach for the detailed characterization of activity in a video sequence. A novel framework for encoding and extracting representative, repeating segments of activities is presented, resulting in "Full Action Instances". We focus on the analysis of human activities, however the proposed algorithm can be extended to more general categories of action that containsrepetitive components, due to its general design. © 2009 IEEE

Sparse representations for hand gesture recognition

Dynamic recognition of gestures from video sequences is a challenging task due to the high variability in the characteristics of each gesture with respect to different individuals. In this work, we propose a novel representation of gestures as linear combinations of the elements of an overcomplete dictionary, based on the emerging theory of sparse representations. We evaluate our approach on a publicly available gesture dataset of Palm Grafti Digits and compare it with other state-of-the-art methods, such as Hidden Markov Models, Dynamic Time Warping and the recently proposed distance metric termed Move-Split-Merge. Our experimental results suggest that the proposed recognition scheme offers high recognition accuracy in isolated gesture recognition and a satisfying robustness to noisy data, thus indicating that sparse representations can be successfully applied in the field of gesture recognition. © 2013 IEEE