4,617 research outputs found
A Novel Method of Violated Constraint Prediction with Modified Spatial Analysis based Fuzzy Sorting
Mobility Prediction of a Moving Node and Network Delay is an important performance characteristic of a wireless network. The Data delivery Delay of a network specifies how long it takes for a data to travel across the network from one node or endpoint to another. It is typically measured in multiples or fractions of seconds. The work presented here belongs to domain of data mining cum wireless network , the Real Time Early Prediction of network delay based on mobility is done using the proposed spatial analysis for constraint violation prediction method. A New application is presented concerning the Delivery delays of UDP packets in GPRS network. The GPS points that are collected from GPS module is analyzed using proposed spatial analysis, for future location prediction using Timestamps as primary data
A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage
A key aspect of a sustainable urban transportation system is the
effectiveness of transportation policies. To be effective, a policy has to
consider a broad range of elements, such as pollution emission, traffic flow,
and human mobility. Due to the complexity and variability of these elements in
the urban area, to produce effective policies remains a very challenging task.
With the introduction of the smart city paradigm, a widely available amount of
data can be generated in the urban spaces. Such data can be a fundamental
source of knowledge to improve policies because they can reflect the
sustainability issues underlying the city. In this context, we propose an
approach to exploit urban positioning data based on stigmergy, a bio-inspired
mechanism providing scalar and temporal aggregation of samples. By employing
stigmergy, samples in proximity with each other are aggregated into a
functional structure called trail. The trail summarizes relevant dynamics in
data and allows matching them, providing a measure of their similarity.
Moreover, this mechanism can be specialized to unfold specific dynamics.
Specifically, we identify high-density urban areas (i.e hotspots), analyze
their activity over time, and unfold anomalies. Moreover, by matching activity
patterns, a continuous measure of the dissimilarity with respect to the typical
activity pattern is provided. This measure can be used by policy makers to
evaluate the effect of policies and change them dynamically. As a case study,
we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin
Robotic Mobile Holder (For CAR Dashboards)
In the current smart tech world, there is an immense need of automating tasks and processes to avoid human intervention, save time and energy. Nowadays, mobile phones have become one of the essential things for human beings either to call someone, connect to the internet, while driving people need mobile phones to receive or make a call, use google maps to know the routes and many more. Normally in cars, mobile holders are placed on the dashboard to hold the mobile and the orientation of the phone needs to be changed according to the driver's convenience manually, but the driver may distract from driving while trying to access mobile phone which may lead to accidents. To solve this problem, an auto adjustable mobile holder is designed in such a way that it rotates according to the movement of the driver and also it can even alert the driver when he feels drowsiness. Image Processing is used to detect the movement of the driver which is then processed using LabVIEW software and NI myRIO hardware. NI Vision development module is used to perform face recognition and servo motors are used to rotate the holder in the required position. Simulation results show that the proposed system has achieved maximum accuracy in detecting faces, drowsiness and finding the position coordinates
Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery
A robust and fast automatic moving object detection and tracking system is
essential to characterize target object and extract spatial and temporal
information for different functionalities including video surveillance systems,
urban traffic monitoring and navigation, robotic. In this dissertation, I
present a collaborative Spatial Pyramid Context-aware moving object detection
and Tracking system. The proposed visual tracker is composed of one master
tracker that usually relies on visual object features and two auxiliary
trackers based on object temporal motion information that will be called
dynamically to assist master tracker. SPCT utilizes image spatial context at
different level to make the video tracking system resistant to occlusion,
background noise and improve target localization accuracy and robustness. We
chose a pre-selected seven-channel complementary features including RGB color,
intensity and spatial pyramid of HoG to encode object color, shape and spatial
layout information. We exploit integral histogram as building block to meet the
demands of real-time performance. A novel fast algorithm is presented to
accurately evaluate spatially weighted local histograms in constant time
complexity using an extension of the integral histogram method. Different
techniques are explored to efficiently compute integral histogram on GPU
architecture and applied for fast spatio-temporal median computations and 3D
face reconstruction texturing. We proposed a multi-component framework based on
semantic fusion of motion information with projected building footprint map to
significantly reduce the false alarm rate in urban scenes with many tall
structures. The experiments on extensive VOTC2016 benchmark dataset and aerial
video confirm that combining complementary tracking cues in an intelligent
fusion framework enables persistent tracking for Full Motion Video and Wide
Aerial Motion Imagery.Comment: PhD Dissertation (162 pages
Mechatronic Systems
Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools
3D reconstruction and motion estimation using forward looking sonar
Autonomous Underwater Vehicles (AUVs) are increasingly used in different domains
including archaeology, oil and gas industry, coral reef monitoring, harbour’s security,
and mine countermeasure missions. As electromagnetic signals do not penetrate
underwater environment, GPS signals cannot be used for AUV navigation, and optical
cameras have very short range underwater which limits their use in most underwater
environments.
Motion estimation for AUVs is a critical requirement for successful vehicle recovery
and meaningful data collection. Classical inertial sensors, usually used for AUV motion
estimation, suffer from large drift error. On the other hand, accurate inertial sensors are
very expensive which limits their deployment to costly AUVs. Furthermore, acoustic
positioning systems (APS) used for AUV navigation require costly installation and
calibration. Moreover, they have poor performance in terms of the inferred resolution.
Underwater 3D imaging is another challenge in AUV industry as 3D information is
increasingly demanded to accomplish different AUV missions. Different systems have
been proposed for underwater 3D imaging, such as planar-array sonar and T-configured
3D sonar. While the former features good resolution in general, it is very expensive and
requires huge computational power, the later is cheaper implementation but requires
long time for full 3D scan even in short ranges.
In this thesis, we aim to tackle AUV motion estimation and underwater 3D imaging by
proposing relatively affordable methodologies and study different parameters affecting
their performance. We introduce a new motion estimation framework for AUVs which
relies on the successive acoustic images to infer AUV ego-motion. Also, we propose an
Acoustic Stereo Imaging (ASI) system for underwater 3D reconstruction based on
forward looking sonars; the proposed system features cheaper implementation than
planar array sonars and solves the delay problem in T configured 3D sonars
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