INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES <p>The main objective of <strong>International Journal of Advances in Signal and Image Sciences (IJASIS), e-ISSN: 2457-0370</strong> is to encourage profound research in the areas of science and engineering. IJASIS publishes novel research methods with novel analytics in all fields of science and engineering but not limited to advances in data, signal and image processing and its applications. All submissions are double blind peer reviewed for possible publication.</p> XLESCIENCE en-US INTERNATIONAL JOURNAL OF ADVANCES IN SIGNAL AND IMAGE SCIENCES 2457-0370 ENHANCEMENT OF POWER QUALITY USING FUZZY LOGIC CONTROLLED DSTATCOM <p>End users of electricity want to receive a quality and reliable electric power without interruption all the time throughout year. Even though, power generation station maintains quality, some natural and man-made sources affect the quality of electric power supply being distributed. Various conventional devices are available to improve electric power quality, but their performance is inadequate.&nbsp; The main aim of the work is to design and simulate a system which provides quality and reliable electric power. The system uses Distribution STATic COMpensator (DSTATCOM) to maintain the power at near sinusoidal voltage and at designed frequency. DSTATCOM is a shunt connected Voltage Source Inverter (VSI) based custom power device, used to mitigate power quality issues. The system also uses the fuzzy logic control algorithm, to control the Pulse Width Modulation (PWM) controllers used in VSI of DSTATCOM.</p> Pandiaraj R Rani Hemamalini R Copyright (c) 2020 2020-06-28 2020-06-28 6 1 21 28 10.29284/ijasis.6.1.2020.21-28 S-TRANSFORM AND GAUSSIAN MIXTURE MODEL FOR ACOUSTIC SCENE CLASSIFICATION <p>In this study, Acoustic Scene Classification (ASC) system is designed with the help of <em>S</em>-transform and Gaussian Mixture Model (GMM). The <em>S</em>-transform is an extension of continuous wavelet transform that combines the progressive resolution with phase information. Thus, it exhibits the amplitude response of the frequency samples in contrast to wavelet transform. The <em>S</em>-transform coefficients are modeled by GMM using posterior probabilities of testing features. Also, preprocessing of acoustic signals is done by a series of operations; explosion, pre-emphasis filtration and windowing approach. The number of Gaussian components which is used to model the scene is varied (GMM-4, GMM-8, GMM-16, and GMM-32) and the performance of ASC system is analyzed using TAU Urban Acoustic Scenes 2019. The results show the effectiveness of the system with average recognition rate of 77.59%, 81.58%, 87.66% and 84.50% for GMM-4, GMM-8, GMM-16, and GMM-32 respectively.</p> Santosh Kumar Srivastava Copyright (c) 2020 2020-06-28 2020-06-28 6 1 29 37 10.29284/ijasis.6.1.2020.29-37 ANIMAL HEALTH MONITORING WITH MISSING AND THEFT PREVENTION DEVICE USING WIRELESS SENSOR NETWORK AND INTERNET OF THINGS <p>A device to know the health status of all the animals in a farm along with its missing and theft prevention is proposed in this article. The necessary sensors to sense the health information of animals and the short range wireless communication technology and internet to communicate with the owner of the farm, are planned to use in the devices. This system will be very helpful to diagnose the health condition of all the animals simultaneously and to improve it. The system can prevent the animals from missing or theft. A mobile application which acts as a user interface provides the owner with useful information such as animal’s body temperature, heart rate, rumination and their presence inside the farm. The prototype of this idea is developed and is included for testing. Since the animal health status can be monitored from anywhere in world using a mobile application, the device will ease the work of farm owner and the laborers.</p> Sundaramoorthi P Rajeenamol P.T Anoopkumar M.V Copyright (c) 2020 2020-06-28 2020-06-28 6 1 38 44 10.29284/ijasis.6.1.2020.38-44 SKIN LESION SEGMENTATION BY PIXEL BY PIXEL APPROACH USING DEEP LEARNING <p>Skin lesion segmentation is an imperative step for image analysis and visualization task. Manual segmentation by an expert operator is too time-consuming and its accuracy may be degraded by different human operators. An automatic segmentation method is therefore required and one of the important parts in any classification system. In this work, more accurate skin lesion segmentation by Pixel-by-Pixel (PbP) approach using deep learning is presented. Before employing PbP approach, dermoscopic images are prepared for more accurate segmentation by Top-Hat Transform (THT) which removes the hair in the skin regions. The PbP approach has four stages; study the training images consists of skin lesions, construction of deep learning network followed by training it and finally evaluate the network with testing images. The evaluation of PbP approach is carried out using PH<sup>2</sup> database images. Results of PbP approach in terms of Jaccard Index (JI), Accuracy (Acc) and DIce Coefficients (DIC) show the effectiveness of the system for skin lesion segmentation.&nbsp; &nbsp;&nbsp;&nbsp;</p> Shekaina Justin Manjula Pattnaik Copyright (c) 2020 2020-06-15 2020-06-15 6 1 12 20 10.29284/ijasis.6.1.2020.12-20 PULMONARY EMPHYSEMA ANALYSIS USING SHEARLET BASED TEXTURES AND RADIAL BASIS FUNCTION NETWORK <p>The emergence of High Resolution Computed Tomography (HRCT) images of the lungs clearly shows the parenchymal lung architecture and thus the quantification of obstructive lung disease becomes most accurate. In this study, an automated system to diagnose obstructive lung disease called emphysema is presented using HRCT images of the lungs. The kind of texture information that ideally can be extracted from HRCT images depends on the multi-resolution representation system. The proposed Pulmonary Emphysema Analysis (PEA) system employs Shearlets as it can extracts more texture information than wavelets in different directions and levels. Radial Basis Function Network (RBFN) is employed for the classification of HRCT images into three categories; Normal Tissue (NT), Paraseptal Emphysema (PSE) and Centrilobular Emphysema (CLE). Results prove that a confident diagnosis of pulmonary emphysema is established to help clinicians which will also increase the precision of diagnosis.</p> Wogderes Semunigus Copyright (c) 2020 2020-06-12 2020-06-12 6 1 1 11 10.29284/ijasis.6.1.2020.1-11 WIRELESS SENSOR NETWORK FOR ENVIRONMENTAL MONITORING IN HILL AREAS AS A PREVENTIVE MEASURE OF GLOBAL WARMING <p>Wireless Sensor Network (WSN) is used in various applications such as area monitoring, healthcare monitoring, industrial and environmental monitoring and so on. Spatially dispersing various sensors and creating a network for knowing the physical conditions of the environment is known as WSN. In this paper, WSN is created for monitoring the environment in hill areas for detecting the various abnormalities such as forest fire, deforestation and landslides. Long Range (LoRa) low power wireless technology and sensors for monitoring forest fire, deforestation and landslides are used in WSN. The data collected in the central point is processed for further evaluation and alerts the authorities through SMS and internet. Global System for Mobile (GSM) Communication modem with Global Packet Radio System (GPRS) enabled SIM card is used for enabling internet service for the central data collecting unit. The system is powered from battery which is recharged by electrical energy converted from solar energy using solar panel.</p> Hare Ram Singh Copyright (c) 2019 2019-12-31 2019-12-31 6 1 1 6 10.29284/ijasis.5.2.2019.1-6 ABNORMAL EVENT DETECTION IN PEDESTRIAN PATHWAY USING GARCH MODEL AND MLP CLASSIFIER <p>In computer vision, one of the complex research areas is video surveillance. It is very important to monitor the abnormal events in public places. Due to the technical advances, the usage of cameras is increased for surveillance purpose. As human operators are employed for the observation, their visual attention is reduced after long periods. Hence, an automated Abnormal Event Detection (AED) technique is designed in this study. It uses Generalized Autoregressive Conditional Heteroscedasticity (GARCH) which is a statistical model to model the events occurs in the pedestrian pathway. Before modeling, a series of preprocessing steps are employed to detect the moving objects. Multilayer Perceptron (MLP) is used to classify the parameters of GARCH model as normal event or abnormal event. Results show that the events are modeled by GARCH in an efficient manner which provides promising results for AED.</p> Manjula Pattnaik Copyright (c) 2019 2019-12-31 2019-12-31 6 1 15 22 10.29284/ijasis.5.2.2019.15-22 TEXT LOCALIZATION IN SCENE IMAGES BY BENDELET TRANSFORM <p>In an automated text recognition system, one of the prerequisites is the localization of text.&nbsp; It is a challenging task in scene image due to their background and non uniform size of characters in the images. In this study, an efficient text localization system using bendlet transform is presented. Among the various multi-resolution and multi-directional analysis, bendlet transform has superior property that they classify the curvature precisely. To achieve this property, it uses an addition parameter than shearlets called bending operator.&nbsp; The system decomposes the scene images by bendlet transform and then reconstructs using the bands which contains only the edge information. Then, a series of post processing is applied to locate the text region in a scene image. Results show the robustness of the text localization system by successfully locating the text region in the scene images with different background and non-uniform text sizes.</p> Ajay Kumar Gupta Rama Krishna K Copyright (c) 2019 2019-12-31 2019-12-31 6 1 32 38 10.29284/ijasis.5.2.2019.32-38 SEPARATION AND CLASSIFICATION OF FETAL ECG SIGNAL BY ENHANCED BLIND SOURCE SEPARATION TECHNIQUE AND NEURAL NETWORK <p>The separation of Fetal ElectroCardioGram (FECG) from mother's Abdominal ECG (AECG) signal is complicated and very important in medical diagnosis during pregnancy. In this study, the separation and classification of FECG signal from AECG signals is presented by a novel method. It uses MULTI-COMBI based Blind Source Separation (BSS) technique to separate the FECG signals. The separated FECG signals have different features, which are extracted using the morphological feature extraction method. These extracted features are used for classification by using Feed Forward Neural Network (FFNN). This classifier classifies the FECG into five different classes. The entire work is implemented in MATLAB. Results show that FFNN gives the classification accuracy of 77.1%, sensitivity of 75.3%, and specificity of 76.7%.</p> Felix Joseph Xavier Copyright (c) 2019 2019-12-31 2019-12-31 6 1 7 14 10.29284/ijasis.5.2.2019.7-14 A PARALLEL AND PIPELINED ARCHITECTURE FOR CORDIC ALGORITHM <p>The COordinate Rotation DIgital Computer (CORDIC) algorithm is an efficient algorithm to calculate the iteratively phase and magnitude or the vector rotations in linear, hyperbolic and circular coordinate system. The existing CORDIC method takes less clock frequency with high delay. To overcome this problem, a new version of updated parallel and pipelined architecture is designed without degrading the performance. It provides highest maximum frequency with less delay by splitting the critical path into several smaller delay paths with enhanced circuit processing time. The designed architecture in this study can be used in navigation application. This method is implemented in the Xilinx ISE tool.</p> Ellapan V Sam Alaric J Copyright (c) 2019 2019-12-31 2019-12-31 6 1 23 31 10.29284/ijasis.5.2.2019.23-31