103 research outputs found
Remote Intruder Detection System
The proposed method discussed here secures a precious thing in a secure room and do the surveillance without any human intervention. Any motion around the secure object during a predefined restricted time period is identified instantly and notified to the security personal staying at a remote room via Wi-Fi. The personal at that room is alerted by an alarm and readily the person can view the image of the intruder in the screen of the computer in front of him or her
Embedded Network Test-Bed for Validating Real-Time Control Algorithms to Ensure Optimal Time Domain Performance
The paper presents a Stateflow based network test-bed to validate real-time
optimal control algorithms. Genetic Algorithm (GA) based time domain
performance index minimization is attempted for tuning of PI controller to
handle a balanced lag and delay type First Order Plus Time Delay (FOPTD)
process over network. The tuning performance is validated on a real-time
communication network with artificially simulated stochastic delay, packet loss
and out-of order packets characterizing the network.Comment: 6 pages, 12 figure
ASSESSMENT OF PHYSIOLOGICAL STRAIN IN MALE FOOD CROP CULTIVATORS ENGAGED IN MANUAL THRESHING TASK IN A SOUTHERN DISTRICT OF WEST BENGAL
The impact of rise in ambient temperature is not confined to output; it has an impact on the work performance of human beings associated with occupational activities in informal sector, especially those carried out in the open field under the sky. The agricultural workers are constrained to work manually all through the day irrespective of disparity in working situation existing in the working environment. Hence, there is an urgent need to study the cardiac performance status in terms of indicators of physiological strain of the human resources. In this backdrop, the present study has been undertaken to assess the degree of physiological strain in male food crop cultivators’ (age range 24 - 36 years) engaged in manual threshing (separating the grains from the rice straw by manually - by hand i.e. beating method) during paddy cultivation time. Moreover the magnitude of physiological strain was significantly higher (P < 0.5) during “Boro” type of paddy cultivating time. The result of the study indicated that human resources are indeed subjected to strains, albeit to different degree, as adjudged by the indicators of physiological strain
MultiViz: A Gephi Plugin for Scalable Visualization of Multi-Layer Networks
The process of visually presenting networks is an effective way to understand
entity relationships within the networks since it reveals the overall structure
and topology of the network. Real networks are extremely difficult to visualize
due to their immense complexity, which includes vast amounts of data, several
types of interactions, various subsystems and several levels of connectivity as
well as changes over time. This paper introduces the "MultiViz Plugin," a
plugin for gephi, an open-source software tool for graph visualization and
modification, in order to to visualize complex networks in a multi-layer
manner. A collection of settings are availabe through the plugin to transform
an existing network into a multi-layered network. The plugin supports several
layout algorithms and lets user to choose which property of the network to be
used as the layer. The goal of the study is to give the user complete control
over how the network is visualized in a multi-layer fashion. We demonstrate the
ability of the plugin to visualize multi-layer data using a real-life complex
multi-layer datasets
Adaptive Gain and Order Scheduling of Optimal Fractional Order PI{\lambda}D{\mu} Controllers with Radial Basis Function Neural-Network
Gain and order scheduling of fractional order (FO) PI{\lambda}D{\mu}
controllers are studied in this paper considering four different classes of
higher order processes. The mapping between the optimum PID/FOPID controller
parameters and the reduced order process models are done using Radial Basis
Function (RBF) type Artificial Neural Network (ANN). Simulation studies have
been done to show the effectiveness of the RBFNN for online scheduling of such
controllers with random change in set-point and process parameters.Comment: 6 pages, 12 figure
Texture Synthesis Guided Deep Hashing for Texture Image Retrieval
With the large-scale explosion of images and videos over the internet,
efficient hashing methods have been developed to facilitate memory and time
efficient retrieval of similar images. However, none of the existing works uses
hashing to address texture image retrieval mostly because of the lack of
sufficiently large texture image databases. Our work addresses this problem by
developing a novel deep learning architecture that generates binary hash codes
for input texture images. For this, we first pre-train a Texture Synthesis
Network (TSN) which takes a texture patch as input and outputs an enlarged view
of the texture by injecting newer texture content. Thus it signifies that the
TSN encodes the learnt texture specific information in its intermediate layers.
In the next stage, a second network gathers the multi-scale feature
representations from the TSN's intermediate layers using channel-wise
attention, combines them in a progressive manner to a dense continuous
representation which is finally converted into a binary hash code with the help
of individual and pairwise label information. The new enlarged texture patches
also help in data augmentation to alleviate the problem of insufficient texture
data and are used to train the second stage of the network. Experiments on
three public texture image retrieval datasets indicate the superiority of our
texture synthesis guided hashing approach over current state-of-the-art
methods.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV),
2019 Video Presentation: https://www.youtube.com/watch?v=tXaXTGhzaJ
- …