Incremental Distance Transforms (IDT)

A new generic scheme for incremental implementations of distance transforms (DT) is presented: Incremental Distance Transforms (IDT). This scheme is applied on the cityblock, Chamfer, and three recent exact Euclidean DT (E2DT). A benchmark shows that for all five DT, the incremental implementation results in a significant speedup: 3.4×−10×. However, significant differences (i.e., up to 12.5×) among the DT remain present. The FEED transform, one of the recent E2DT, even showed to be faster than both city-block and Chamfer DT. So, through a very efficient incremental processing scheme for DT, a relief is found for E2DT’s computational burden

From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips

Short internet video clips like vines present a significantly wild distribution compared to traditional video datasets. In this paper, we focus on the problem of unsupervised action classification in wild vines using traditional labeled datasets. To this end, we use a data augmentation based simple domain adaptation strategy. We utilise semantic word2vec space as a common subspace to embed video features from both, labeled source domain and unlablled target domain. Our method incrementally augments the labeled source with target samples and iteratively modifies the embedding function to bring the source and target distributions together. Additionally, we utilise a multi-modal representation that incorporates noisy semantic information available in form of hash-tags. We show the effectiveness of this simple adaptation technique on a test set of vines and achieve notable improvements in performance.Comment: 9 pages, GCPR, 201

Passive Wireless Temperature Sensing in Extreme Harsh Environments

As the technology in the elds of aerospace and the US power generation industry advances, there is a critical need for new extreme high temperature sensing / monitoring technologies to replace the current out-of-date sensing systems. As the operating temperatures of these jet and turbine engines continue to rise over 1000 C, it is vitally important to monitor the extreme high temperatures in these engines for system health monitoring and to achieve greater engine eciencies. We propose a new passive wireless temperature sensor capable of sensing these extreme high temperatures. The sensor uses an LC resonance circuit to measure the temperature through passive wireless communications. A new novel method of capturing large quantities of frequency information from the sensor is proposed and allows for advanced signal processing methods form other applications areas like wireless communi- cations, radar, and radio astronomy to be implemented. The passive wireless LC resonance high temperature sensor was successfully able to sense temperatures up to 700 C

Instructor activity recognition through deep spatiotemporal features and feedforward Extreme Learning Machines

Human action recognition has the potential to predict the activities of an instructor within the lecture room. Evaluation of lecture delivery can help teachers analyze shortcomings and plan lectures more effectively. However, manual or peer evaluation is time-consuming, tedious and sometimes it is difficult to remember all the details of the lecture. Therefore, automation of lecture delivery evaluation significantly improves teaching style. In this paper, we propose a feedforward learning model for instructor's activity recognition in the lecture room. The proposed scheme represents a video sequence in the form of a single frame to capture the motion profile of the instructor by observing the spatiotemporal relation within the video frames. First, we segment the instructor silhouettes from input videos using graph-cut segmentation and generate a motion profile. These motion profiles are centered by obtaining the largest connected components and normalized. Then, these motion profiles are represented in the form of feature maps by a deep convolutional neural network. Then, an extreme learning machine (ELM) classifier is trained over the obtained feature representations to recognize eight different activities of the instructor within the classroom. For the evaluation of the proposed method, we created an instructor activity video (IAVID-1) dataset and compared our method against different state-of-the-art activity recognition methods. Furthermore, two standard datasets, MuHAVI and IXMAS, were also considered for the evaluation of the proposed scheme.We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for research work carried in Centre for Computer Vision Research (C2VR) at University of Engineering and Technology Taxila, Pakistan. Sergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for Research, Technological Development and Demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509), and Banco Santander.We are also very thankful to participants, faculty, and postgraduate students of Computer Engineering Department who took part in the data acquisition phase.Without their consent, this work was not possible

Predicting failure behavior of polymeric and asphalt composites using damage models

This study investigates the failure behavior of two types of composite materials using damage models within the framework of ABAQUS finite element software. The failure behavior of an IM7/977-2 carbon epoxy composite material subjected to a Mode I delamination is predicted using traction-separation and bulk material damage models that are based on disturbed state concept (DSC) principles. The models were validated by comparing the results to referenced laboratory testing performed on IM7/977-2 carbon epoxy composite. The damaged states associated with various stages of loading are presented in this study. This study also predicts the failure behavior of asphalt materials through the use of damage models using the principles of the DSC. Traction-separation crack response, damage initiation and damage evolution behavior are investigated by modeling pavement systems consisting of a hot mix asphalt (HMA) overlay above an existing HMA layer and subjected to an applied static wheel loading. Preexisting cracks located within the existing asphalt material are also considered. The extended finite element method (XFEM) was employed to model mesh-independent cracking. The finite element model was validated by comparing the results to indirect tensile laboratory testing and referenced direct tensile laboratory data-based results performed on asphalt samples. The validated model was then used to examine damage in a pavement system with and without preexisting cracks

The Case for Liberal Spectrum Licenses: A Technical and Economic Perspective

The traditional system of radio spectrum allocation has inefficiently restricted wireless services. Alternatively, liberal licenses ceding de facto spectrum ownership rights yield incentives for operators to maximize airwave value. These authorizations have been widely used for mobile services in the U.S. and internationally, leading to the development of highly productive services and waves of innovation in technology, applications and business models. Serious challenges to the efficacy of such a spectrum regime have arisen, however. Seeing the widespread adoption of such devices as cordless phones and wi-fi radios using bands set aside for unlicensed use, some scholars and policy makers posit that spectrum sharing technologies have become cheap and easy to deploy, mitigating airwave scarcity and, therefore, the utility of exclusive rights. This paper evaluates such claims technically and economically. We demonstrate that spectrum scarcity is alive and well. Costly conflicts over airwave use not only continue, but have intensified with scientific advances that dramatically improve the functionality of wireless devices and so increase demand for spectrum access. Exclusive ownership rights help direct spectrum inputs to where they deliver the highest social gains, making exclusive property rules relatively more socially valuable. Liberal licenses efficiently accommodate rival business models (including those commonly associated with unlicensed spectrum allocations) while mitigating the constraints levied on spectrum use by regulators imposing restrictions in traditional licenses or via use rules and technology standards in unlicensed spectrum allocations.