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A Deterministic Key-Frame Indexing and Selection for Surveillance Video Summarization
Video data is voluminous and impacts the data storage devices as there are CCTV surveillance videos being created every minute and stored continuously. Due to this increase in data there is a need to create semantic information out of the frames that are being stored. Video Summarization is a process that continuously monitors changes and helps in reducing the number of frames being stored. This work enables summarization to be carried out based on selecting threshold-based system that can select key-frames ideally suit for storage and further analysis. Initially a Global threshold based on Otsus method is carried out for all frames of a surveillance video and based on the set threshold a retrospective comparison is done on each frame based on statistical methods to converge on determining the keyframes. A similarity index is generated based on the iterative comparison of frames based on global and local threshold comparison. The local threshold is indexed based on Analysing Method Patterns to Locate Errors(AMPLE), An-derbergs D(AbD), Cohens Kappa(CK), Tanimoto Similarity(TS), Tversky feature contrast model(TFCM), Pearson coefficient of mean square contingency(Pmsc). The Global threshold is updated each time a keyframe is selected based on the comparison of local and global threshold. The results are compared with five surveillance videos and six methods to identify keyframes Selection Rate is the metric used for calculating the performance. 2019 IEEE. -
Non invasive methods of blood glucose measurement: Survey, challenges, scope
Noninvasive body parameters monitoring and disease detection is one of the emerging research area now a days. In this paper a review on Non-invasive methods of blood glucose measurement has been made. A comparative study has been made which describes the methodology incorporated in the published literatures, research challenges and the used tools. This paper also describes about the factors which highly impacts the non-invasive measurement. Finally, a deep learning based noninvasive measurement method compatible with IOT is mentioned. This paper serves as a proper reference for future researchers working in non-invasive blood glucose measurement domain in selecting appropriate non-invasive method algorithm for glucose monitoring non-invasively. 2019 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth. -
Accident Detection Using Convolutional Neural Networks
Accidents have been a major cause of deaths in India. More than 80% of accident-related deaths occur not due to the accident itself but the lack of timely help reaching the accident victims. In highways where the traffic is really light and fast-paced an accident victim could be left unattended for a long time. The intent is to create a system which would detect an accident based on the live feed of video from a CCTV camera installed on a highway. The idea is to take each frame of a video and run it through a deep learning convolution neural network model which has been trained to classify frames of a video into accident or non-accident. Convolutional Neural Networks has proven to be a fast and accurate approach to classify images. CNN based image classifiers have given accuracy's of more than 95% for comparatively smaller datasets and require less preprocessing as compared to other image classifying algorithms. 2019 IEEE. -
LENN: Laplacian Probability Based Extended Nearest Neighbor Classification Algorithm for Web Page Retrieval
Web page prediction is the area of interest that enables to tackle the problem of dealing with the massive collection of the web pages, mainly, in retrieving the highly relevant web pages. The hectic challenge of the web page prediction methods relied on time-effective and cost-effective management. The problem of dealing with the issue is tackled using the efficient web page retrieval algorithm. The paper proposes a new classifier called, Laplacian probability based Extended Nearest Neighbor (LENN)that is formed through the integration of the Laplacian probability with the Extended Nearest Neighbor (ENN)classifier. The proposed LENN classifier determines the nearest web pages of the query. Accordingly, the web page retrieval is done in three important steps, such as pre-processing, feature indexing and web page retrieval. The key words are stored in the database for performing the feature match such that the highly relevant web page is retrieved based on the maximum value of the score. The experimentation using five benchmarks prove that the proposed method is effective compared with the existing methods of web page retrieval. The maximum precision, recall, and F-measure of the proposed method is found to be 98%, 96.7%, and 97.3%, respectively. 2019 IEEE. -
Recognition of Green Colour Vegetables' Images Using an Artificial Neural Network
Image processing is used in all the domains including agriculture. In this paper, we have introduced a computationally simple and small feature vector, as a tool for the recognition of green colour vegetable images. The RGB colour system is used and the feature set is computationally economic and performs well on locally available vegetable images. For recognition of vegetable images, an ANN-based classifier is deployed. The recognition percentage is in the scale of 74-100 for 15 vegetable types. This work finds application in the packing of vegetables, food processing, automatic vending. 2019 IEEE. -
Impact of Variable Distributed Generation on Distribution System Voltage Stability
With advances in renewable energy (RE)technologies and the promotion of restructuring, distributed energy (DG)sources play a vital role in today's power sector. From the technical and economic point of view, DG sources provide a no of benefits such as lesser system losses, better system voltage profile and lower line congestion. The aim of this work is to determine the voltage stability of a distribution system at different levels of DG compensation determined as a percentage of the total load on the system. The objective function is formulated to minimize the real power loss. At first, the locations are chosen based on strategy using Loss Sensitivity Factors (LSF)and the optimal sizing of multiple units of DG sources is optimized using Particle Swarm Optimization (PSO)algorithm. The simulations are performed on standard IEEE 33-bus and 69-bus test systems and the results validate the importance of placing appropriately sized DG sources at suitable locations to achieve improved voltage stability and reduced distribution losses. 2019 IEEE. -
Universal Electrical Motor Acoustic Noise Reduction based on Rotor Surface Modification
Electromagnetic noise is referred to the audible sound which is produced by materials vibrating due to electromagnetic force. In the present day circumstances, a greater attention is being given to the electromagnetic acoustic noise produced by electrical machines. It is found to annoy human beings and other living organisms due to its tonal sound. The current work aims at designing a rotor for a universal motor with the objective to decrease the acoustic noise by minimization of forced density harmonics. The design consists of some irregularities in the rotor surface to decrease the acoustic noise by internally modifying the air gap permeance. Simulation shall be carried out based on FEM. A lot of research is being carried out on the methods of reducing the noise from electrical machines. The results of the current work significantly help in reducing a lot of noise pollution. The change in the rotor surface will reduce the electromagnetic acoustic noise from the electrical machine. It will also affect the torque parameters positively as studied from earlier research work. 2019 IEEE. -
A fuzzy computing software quality model
Expectation of the quality of a software varies from user to user. A fuzzy approach to measure the quality of a software is very appropriate so that it can deal with non-crisp aspects of the various parameters. In the proposed model, ordered intuitionistic fuzzy soft sets (OIFSS) and relative similarity measures of OIFSS are considered in the backdrop of fuzzy multiple criteria decision making (FMCDM) approach. 2019 Author(s). -
Sentiment Analysis on Time-Series Data Using Weight Priority Method on Deep Learning
Sentiment Analysis (SA)is the process to gain an overview of the public opinion on certain topics and it is useful in commerce and social media. The preference on certain topics can be varied on different time periods. To analyze the sentiments on topics in different time periods, priority weight based deep learning approaches like Convolutional-Long Short-Term Memory (C-LSTM)and Stacked- Long Short-Term Memory (S-LSTM)is explored and analyzed in this research. The research method focuses on three phases. In the first phase text data (review given by the customers on various products)is collected from social networking e-commerce site and temporal ordering is done. In the second phase, different deep learning models are created and trained with different time-series data. In the final phase the weights are assigned based on temporal aspect of the data collected. For the obtained results verification and validation processes are carried out. Precision and recall measures are computed. Results obtained shows better performance in terms of classification accuracy and F1-score. 2019 IEEE. -
Linear and non-linear analysis of solute-magneto convection in a couple stress fluid with porous medium under concentration modulation
The effect of concentration modulation and magnetic field in a couple stress fluid with porous medium and salted from above is studied using linear and non-linear analysis. Venezian approach based on perturbation method is used to obtain the expression for solute Rayleigh number and correction solute Rayleigh number. The expression for correction solute Rayleigh number is obtained as a function of couple stress parameter, Chandrashekar number, Darcy number and Schmidt number. The effect of parameters on symmetric and asymmetric concentration modulation are discussed in the paper. A non-autonomous Ginzburg-Landau equation with time periodic co-efficient is obtained to study the effect of parameters on mass transfer. It is found that onset of convection and mass transfer can be delayed or advanced by varying the parameters of the problem. Asymmetric modulation is found to be more stable than the symmetric modulation. 2019 Author(s). -
An Efficient HOG-Centroid Descriptor for Human Gait Recognition
Automatic recognition of human gait have gained much attention nowadays. Histogram of Oriented Gradient (HOG) is a widely adopted descriptor for object's shape analysis. In this paper, combination of HOG descriptor with silhouette centroid for human gait recognition is proposed. The resultant descriptor, namely HOG-Centroid, achieves better recognition performance on comparison with HOG descriptor individually as well as other existing gait recognition methods. Experiments are carried out with CASIA gait dataset B and cumulative matching scores of 95.3%, 98.1% and 99.2% are obtained for rank 1, rank 5 and rank 10 respectively. 2019 IEEE. -
A Dual Step Strategy for Retinal Thin Vessel Enhancement/Extraction
Blood vessel extraction from retinal images is a challenging and fundamental step in pathological analysis. Most of the vessel extraction algorithms face difficulty in the extraction of thin vessels. In this paper, a dual step strategy for retinal thin vessel enhancement/extraction is proposed. Since thin vessel pixels have intensities closer to the background non-vessel pixels, the first level enhancement algorithms usually suffers in its accurate extraction. This led to explore a novel idea of eliminating the effects of thick vessel pixels in a reference image, via replacing it with neighboring non-vessel pixels. By applying second level enhancement on the vessel subtracted image, thin vessels are projected and improvement in extraction is attained subjectively as well as objectively. 2019 IEEE. -
Fuzzy based Controller for Bi-Directional Power Flow Regulation for Integration of Electric Vehicles to PV based DC Micro-Grid
Utilization of Electric Vehicle as an auxiliary power source to a DC micro-grid for active power regulation is examined here. This paper focus on development of a Fuzzy based controller capable of regulating the bi-directional active power flow between a 10 kW DC Micro-grid and an Electric Vehicle. The system enables to balance the load on grid by performing peak shaving during peak hours and valley filling during off-peak hours. The load curve of Bangalore city for a typical day was taken as the reference and was used to implement the power flow control. The DC grid was designed for a 10 kW PV based micro-grid. The integrated DC micro-grid was simulated on MATLAB/Simulink platform and the obtained characteristics demonstrate that the power flow from grid to vehicle and vehicle to grid during the peak and off-peak periods respectively. The auxiliary battery pack was stressed only to 10.7 % of its 1C-rating leaving scopes for higher level power transmission possible between the systems. 2019 IEEE. -
Securing Provenance Data with Secret Sharing Mechanism: Model Perspective
Elicitation about the genesis of an entity is referred to as provenance. With regards to data objects and their relationships the same is termed as data provenance. In majority of the instances, provenance data is sensitive and a small variation or adjustment leads to change in the entire chain of the data connected. This genesis needed to be secured and access is granted for authorized party. Individual control in preserving the privacy of data is common scenario and there are a good number of approaches with respect to cryptography. We propose a unique model, wherein the control of the data is available with multiple bodies however not with one; and when an access has to be granted for a genuine purpose, all the bodies holding their share will have to agree on a common platform. Combining these shares in a peculiar pattern allows the grant for accessing data. The method of allocating control to multiple bodies and allowing grant based on combining stakes is called as secret sharing mechanism. Division of the shares can be drawn from visual encryption approach. It provides transparencies for a given input message. This paper throws light on a framework associated to securing provenance via secret sharing security notion. 2019 IEEE. -
A Comparative Study of Spectral Indices for Surface Water Delineation Using Landsat 8 Images
Surface water delineation is an important step in performing change detection studies on water bodies with the help of multispectral images. Commonly used techniques for surface water delineation from multispectral images are single band density slicing, spectral index based, machine learning based classification and spectral unmixing based methods. This paper presents a comparative study of commonly used spectral indices Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Water Ratio Index (WRI), Normalized Difference Forest Index (NDFI), Enhanced water Index (EWI), Weighted Normalized Difference Water Index (WNDWI), Automated Water Extraction Index (AWEI), Tasseled Cap Water Index (TCW), Global Water Index (GWI)and Sum457 that were developed for water detection for their suitability and effectiveness when applied on Landsat 8 images. While all the above mentioned indices showed their usefulness in water detection, simpler and faster indices like GWI and Sum457 yielded comparable results to that of more complex ratios like EWI and WNDWI. 2019 IEEE. -
Experimental study of solar dryer used for drying chilly and ginger
Open air solar drying is one of the most popular methods for drying food products holds many drawbacks resulting in contamination of food products. This project is to transform the traditional method to an innovative, clean and cost-effective method to dry chilly and ginger, two being the top export commodity of India. Here a solar dryer is made which comprised of flat plate air heater, a chamber for drying and an air blower which induces forced convection. This system enhances the drying process even at low-intensity sunlight by assimilating heat storage materials. The equipment was tested in the meteorologicalcondition of the faculty of engineering, Christ (Deemed to be University) (latitude of 12.86N, a longitude of 77.43E) Bangalore, Karnataka. The process has reduced the moisture content from around 72.69% to 28.24% in the case of chilly and from 68.88% to 14.31% in the case of ginger within a period of 10 hours for a mass flow rate of 0.051kg/s. Average drier efficiency was estimated to be about 22%. The specific moisture extraction rate was estimated to be about 0.76 kg/kWh. This process resulted in a better moisture extraction rate, eliminating the defects caused by open sun drying. This process resulted in a better moisture extraction rate, eliminating the defects caused by open sun drying. 2019 Author(s). -
A Study on Machine Learning Techniques for Internet of Things in Societal Applications
Until recent years, monitoring and analysing system inputs, responses were merely based on Sensor Systems. Gradually, Embedded Systems and other Data Resources including Remote Monitoring Units started gaining momentum. But, with advent of Internet of Things (IoT), the outlook and expectations are broadened. IoT introduced incredible volumes of structured and unstructured data of different formats. There is a need to investigate, the underlying concepts of Machine Learning, Internet of Things (IoT) and Embedded Systems. These domains grow and expand its frontiers at a very fast pace. This paper attempts to throw light on possibilities of combining different technological domains, for design and development of Smarter and Context Aware Intelligent Electronics Systems for Societal Utility. Effective implementation and realization of such systems by suitable fusion of essential inter-disciplinary concepts is expected to have considerable potential for societal impact in the years to come. 2019 IEEE. -
Characterization of interval-valued fuzzy bridges and cutnodes
In this paper, we characterize interval - valued fuzzy bridges and interval-valued fuzzy cutnodes in terms of ? strong arcs. We discuss about the behaviour of arcs in a strongest path of an interval - valued fuzzy graph. An example is provided to prove that strongest paths are not in general related to strong paths in an interval - valued fuzzy graph. Finally we give a particular condition under which strong paths and strongest paths are equivalent. 2019 Author(s). -
Improved File Security System Using Multiple Image Steganography
Steganography is the process of hiding a secret message within an ordinary message extracting it at its destination. Image steganography is one of the most common and secure forms of steganography available today. Traditional steganography techniques use a single cover image to embed the secret data which has few security shortcomings. Therefore, batch steganography has been adopted which stores data on multiple images. In this paper, a novel approach is proposed for slicing the secret data and storing it on multiple cover images. In addition, retrieval of this secret data from the cover images on the destination side has also been discussed. The data slicing ensures secure transmission of the vital data making it merely impossible for the intruder to decrypt the data without the encrypting details. 2019 IEEE. -
Despeckling of Ultra sound Images using spatial filters - A Fusion Approach
Ultra sound images are normally affected by speckle noise which is typically multiplicative in nature. This study proposes different fusion based despeckling methods for ultra sound images. The output of existing spatial domain despeckling methods viz. Lee filter, Bayesian Non Local Means (BNLM) filter and Frost filter are fused pairwise. Fusion is implemented in two steps, first an inter-scale stationary wavelet coefficient fusion followed by an intra-scale wavelet coefficient fusion. Analysis of these projected despeckling strategies are conducted using metrics like Peak Signal to Noise Ratio (PSNR), Equivalent Number of Looks (ENL), Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI). The results show that the performance of fusion based methods is better than the respective individual filters for despeckling ultra sound images. 2019 IEEE.