Autonomous Wireless Radar Sensor Mote for Target Material Classification

An autonomous wireless sensor network consisting of different types of sensor modalities is a topic of intense research due to its versatility and portability.These types of autonomous sensor networks commonly include passive sensor nodes such as infrared,acoustic,seismic and magnetic.However,fusion of another active sensor such as Doppler radar in the integrated sensor network may offer powerful capabilities for many different sensing and classification tasks.In this work,we demonstrate the design and implementation of an autonomous wireless sensor network integrating a Doppler sensor into wireless sensor node with commercial off the shelf components.We also investigate the effect of different types of target materials on return radar signal as one of the applications of the newly designed radar-mote network.Usually type of materials can affect the amount of energy reflected back to the source of an electromagnetic wave.We obtain mathematical and simulation models for the reflectivity of different homogeneous non-conducting materials and study the effect of such reflectivity on different types of targets.We validate our simulation results on effect of reflectivity on different types of targets using real toy experiment data collected through our autonomous radar-mote sensor network

Autonomous Discovery and Maintenance of Mobile Frees-Space-Optical Links

Free-Space-Optical (FSO) communication has the potential to play a significant role in future generation wireless networks. It is advantageous in terms of improved spectrum utilization, higher data transfer rate, and lower probability of interception from unwanted sources. FSO communication can provide optical-level wireless communication speeds and can also help solve the wireless capacity problem experienced by the traditional RF-based technologies. Despite these advantages, communications using FSO transceivers require establishment and maintenance of line-of-sight (LOS). We consider autonomous mobile nodes (Unmanned Ground Vehicles or Unmanned Aerial Vehicles), each with one FSO transceiver mounted on a movable head capable of scanning in the horizontal and vertical planes. We propose novel schemes that deal with the problems of automatic discovery, establishment, and maintenance of LOS alignment between these nodes with mechanical steering of the directional FSO transceivers in 2-D and 3-D scenarios. We perform extensive simulations to show the effectiveness of the proposed methods for both neighbor discovery and LOS maintenance. We also present a prototype implementation of such mobile nodes with FSO transceivers. The potency of the neighbor discovery and LOS alignment protocols is evaluated by analyzing the results obtained from both simulations and experiments conducted using the prototype. The results show that, by using such mechanically steerable directional transceivers and the proposed methods, it is possible to establish optical wireless links within practical discovery times and maintain the links in a mobile setting with minimal disruption

Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository

Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most efficient and widely used supervised learning algorithms are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor (LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this paper is to implement these elegant learning algorithms on eleven different datasets from the UCI machine learning repository to observe the variation of accuracies for each of the algorithms on all datasets. Analyzing the accuracy of the algorithms will give us a brief idea about the relationship of the machine learning algorithms and the data dimensionality. All the algorithms are developed in Matlab. Upon such accuracy observation, the comparison can be built among KNN, SVM, LMNN, and ENN regarding their performances on each dataset.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced performances for clusters with different densities. Therefore, in this paper, an adaptive DBSCAN is proposed which can work significantly well for identifying clusters with varying densities.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

INVESTIGATION OF THE BULK, SURFACE AND TRANSFER PROPERTIES OF CHLORINE BLEACHED DENIM APPAREL AT DIFFERENT CONDITION

Oxidizing bleaching agent (calcium hypochlorite or bleaching powder) is widely used in the apparel washing plant as a color removing and cost effective finishing chemical. As the faded denim or old look denim is preferred by the today’s youth it has become a crucial issue for the technologists to modify denim apparel to fulfill the demand of existing trend. Calcium hypochlorite (Ca(OCl)Cl) fades the denim effectively but a significant changes are happened in the properties of the denim apparel. The main objective of this paper was to investigate the changes of bulk, surface and transfer properties of denim apparel after the chlorine bleach action at varying length of washing time (10,15 and 30 min) with fixed concentration and temperature (50°C). These properties are related to the performance of the end product. 100% cotton indigo dyed 2/1 twill denim apparel was treated with 5gm/l (Ca(OCl)Cl). To determine the end use performance of the modified denim the changes of tensile strength, stiffness, dimensional stability (bulk properties), hand roughness, rubbing fastness (surface properties), air permeability, water repellency (transfer properties) of the modified denim apparel were evaluated. It has been monitored from the experimental data that the bulk properties play down but the surface properties have a noticeable improvement after the chlorine bleach action. It is also noticed that washing time has a significant effect on air permeability of the treated denim apparel

INVESTIGATION OF THE BULK, SURFACE AND TRANSFER PROPERTIES OF CHLORINE BLEACHED DENIM APPAREL AT DIFFERENT CONDITION

Oxidizing bleaching agent (calcium hypochlorite or bleaching powder) is widely used in the apparel washing plant as a color removing and cost effective finishing chemical. As the faded denim or old look denim is preferred by the today’s youth it has become a crucial issue for the technologists to modify denim apparel to fulfill the demand of existing trend. Calcium hypochlorite (Ca(OCl)Cl) fades the denim effectively but a significant changes are happened in the properties of the denim apparel. The main objective of this paper was to investigate the changes of bulk, surface and transfer properties of denim apparel after the chlorine bleach action at varying length of washing time (10,15 and 30 min) with fixed concentration and temperature (50°C). These properties are related to the performance of the end product. 100% cotton indigo dyed 2/1 twill denim apparel was treated with 5gm/l (Ca(OCl)Cl). To determine the end use performance of the modified denim the changes of tensile strength, stiffness, dimensional stability (bulk properties), hand roughness, rubbing fastness (surface properties), air permeability, water repellency (transfer properties) of the modified denim apparel were evaluated. It has been monitored from the experimental data that the bulk properties play down but the surface properties have a noticeable improvement after the chlorine bleach action. It is also noticed that washing time has a significant effect on air permeability of the treated denim apparel