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