Volume-9 Issue-1S3, December 2019 – International Journal of Engineering and Advanced Technology (IJEAT)

A10901291S319

IJEAT Journal — Wed, 15 Mar 2023 11:54:52 +0000

The International Journal of Engineering and Advanced Technology (IJEAT) has ISSN: 2249-8958 (online), an open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in February, April, June, August, October and December. 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A Robust Watermarking Method for an Authentication of Video Surveillance Applications
Satish D. Mali¹, AgilandeeswariL²
¹Sathish D Mali, Puranmal Lahoti Government Polytechnic, Latur (Maharashtra), India.
²Dr. L. Agilandeeswari, Associate Professor, Department of Information Technology and Engineering, VIT University, Vellore (Tamil Nadu), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 500-506 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10901291S319/19©BEIESP | DOI: 10.35940/ijeat.A1090.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The problem of authenticating the video content is the major role of any automated video surveillance systems (AVS). This research work delivers an approach to authenticate the surveillance video which can be used by attorneys to prove their clients using Digital Watermarking. The digital watermark gives an assurance that the provided video surveillance frame is not tampered. In this paper, we proposed a robust video watermarking approach for an authentication of video surveillance applications. The digital watermark is embedded into the Discrete Wavelet domain of the identified key frames using holoentropy. Here, the pixel map will be optimallygenerated for the watermarking and the image embedding using the proposed classifier using Moth – flame – Rider optimization based Neural Network (MF-ROA-based NN) will generate the optimal map prediction based on the fitness measure, such as wavelet coefficient, energy, entropy, loop coefficient, and standard deviation, respectively. This optimal map is used for both embedding and extraction. The experimental results proves that the proposed systems can authenticate the surveillance video frames against various attacks when compared to the existing systems.
Keywords: Automated Video Surveillance Systems (AVS), Digital Watermarking, Discrete Wavelet Domain, Holoentropy, Optimal Map.
Scope of the Article: Authentication, Authorization, Accounting

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A10891291S319

IJEAT Journal — Wed, 15 Mar 2023 11:46:33 +0000

Adaboost Cascade Classifier for Classification and Identification of Wild Animals using Movidius Neural Compute Stick
S. Divya Meena¹, L. Agilandeeswari²
¹S. Divya Meen, Research Scholar, Department of Information Technology and Engineering, VIT University, Vellore (Tamil Nadu), India.
²Dr. L. Agilandeeswari, Associate Professor, Department of Information Technology and Engineering, VIT University, Vellore (Tamil Nadu), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 495-499 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10891291S319/19©BEIESP | DOI: 10.35940/ijeat.A1089.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Deep learning has gone deeper and has been utilized in almost all the applications like object recognition, image classification, speech recognition and much more. Most of the real-time applications rely on deep learning for accurate results. But one downside to the deep learning is its demand for GPUs (Graphical Processing Unit) or TPUs (Tensor Processing Units) for faster execution. There was no one-stop-shop hardware and software for deep learning applications, until recently Intel launched the Movidius Neural Compute Stick (NCS). This sleek device provides the power of GPU in a CPU based system. In this work, we have modeled an animal detection system using NCS and AdaBoost classifier powered by Multi-Block Local Binary Pattern (MB-LBP) features. The model has been built upon AlexNet and has achieved an average accuracy of 96.8% and a false rate 2.3% in classifying the animals as wild and non-wild. Furthermore, the model has a speedup of 467% when compared to the execution in the CPU based system.
Keywords: Deep Neural Network, Alex Net, Movidius Neural Compute Stick, MP-LBP, AdaBoost Classifier.
Scope of the Article: Classification

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A10881291S319

IJEAT Journal — Wed, 15 Mar 2023 11:39:22 +0000

A Segmentation Method of Fused Multispectral and Panchromatic Images using Quick Shift Algorithm and Brovey Transform
Vanmathi C
Vanmathi C, Department of Information Technology and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 486-494 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10881291S319/19©BEIESP | DOI: 10.35940/ijeat.A1088.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Satellite Image Processing has always been a good domain to carry out researches. This is mainly due to the probability of the vast nature of the domain. There are many Image fusions and segmentation methods are available in the area of Satellite Image Processing. The presented research work proposes the image fusion method to inter twined the high determination panchromatic (PAN) picture and the low-determination multispectral picture to gain the multispectral image of high determination. For picture fusion the Brovey transform is used. The derived inter twined picture is granted as an input to the further segmentation process. Segmentation of the image obtained after the fusion is done and the recognition of land, marine, plantation and residential ranges are shown in the picture of the particular location gained from the satellite. For the segmentation of the fused satellite picture the Quick Shift Segmentation clustering technique is used. Utilizing Quick Shift Segmentation on the combined image the various sections can easily be identified as compared to the unprocessed picture gain from the satellite. Scientists utilize Brovey transform to intertwined RGB colour pictures to high determination panchromatic (PAN) pictures. In the proposed work, the main focus is to degrade spectral irregularity and enhancement of spatial data on merging the pictures of satellite Limitation of the current technique of the combination of images for example restricted directionality, shift invariant, absence of the data are amended by using Brovey transformation. The outcomes of the current system are generally unsatisfied. To overcome this Quick Shift Segmentation technique is used with the Brovey transform in the proposed system. The exactness of segmented image is checked with respect to reference information. The fused image segmentation outcomes have been ascertained and it shows that Brovey transform with interaction of Quick Shift Segmentation technique provides more successive outcomes.
Keywords: Brovey Transform, Quick Shift Segmentation Technique, Satellite Image Processing, Panchromatic and MS Images.
Scope of the Article: Algorithm Engineering

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A10871291S319

IJEAT Journal — Wed, 15 Mar 2023 11:31:53 +0000

Towards an Automated Testing Framework for Big Data
Ramanathan Meyappan¹, Nachiyappan S², Disha Nair³
¹Ramanathan Meyappan, TCS, Delaware, USA.
²Nachiyappan S, SCSE VIT Chennai (Tamil Nadu), India.
³Disha Nair, Chase Bank, Wilmington USA.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 479-485 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10871291S319/19©BEIESP | DOI: 10.35940/ijeat.A1087.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Big data testing services are to deliver end to end testing methodologies which address our big data challenges. The testing module includes two types of functionalities. One is functional testing and second is non- functional testing. The functional testing should be accomplished at every stage of big data processing. Functional testing is nothing but the big data sources extraction testing, data migration testing and big data ecosystem. Testing which completes ETL test strategy, Map job reduce validation, multicore Data integration validation and data duplication check. On the other side the non-functional testing is to ensure that there are no quality defeat in data and no performance related issues. It covers the area for security testing, performance testing which solve the problem of monitoring and identify bottlenecks.
Keywords: Big Data, Functional Testing, Non-Functional Testing, Data Duplication Check.
Scope of the Article: Big Data Analytics and Business Intelligence

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A10861291S319

IJEAT Journal — Wed, 15 Mar 2023 11:24:20 +0000

An Efficient Method for Multiple Face Detection & Recognition
Pritthish Chattopadhyay¹, Priyasha Agarwalla², Vaibhav Sancheti³, Srikari Sharma⁴, Swarnalatha P⁵
¹Pritthish Chattopadhyay, Department of Computer Sciences & Engineering, VIT University, Vellore (Tamil Nadu), India.
²Priyasha Agarwalla, Department of Computer Sciences & Engineering, VIT University, Vellore (Tamil Nadu), India.
³Vaibhav Sancheti, Department of Computer Sciences & Engineering, VIT University, Vellore (Tamil Nadu), India.
⁴Srikari Sharma, Department of Computer Sciences & Engineering, VIT University, Vellore (Tamil Nadu), India.
⁵Swarnalatha P, Department of Computer Science & Engineering, VIT University, Vellore (Tamil Nadu), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 474-478 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10861291S319/19©BEIESP | DOI: 10.35940/ijeat.A1086.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The problem of Face detection and recognition is becoming a challenge due to the wide variety of faces and the complexity of the noises and background of image. In this paper we have used C-sharp and Haar algorithm to detect the face. First in this paper the image is taken with a web-camera, storing it in the database and then once again when the person comes in the frame the name of the person is displayed. This paper is done in C-sharp which was a bit difficult for us to do and we have combined both the face detection and the recognition. The proposed method has good output and a good recognition rate. The limitation of the paper is that it does not display the name of the person above the face. In the future work will be carried on the above said topic. While developing the code some sample codes in python but those were basic programs. So this paper aims to find a solution for it and developed in C-sharp. In finding the XML file of haarcascade frontal face detection we found some problems and had to do a bit of research in finding it. The code for face detection and face recognition were found in different places and in this paper the codes for the both has been combined and found some difficulty. To overcome the basic programs we have written the code in C-sharp and the difficulty which we faced in combining the two codes have been solved. The solution has been successfully implemented and the code is fully running and the output has been successfully achieved.
Keywords: Haarcascade, Frontal Face Detection, C- Sharp, XML.
Scope of the Article: Pattern Recognition

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A10851291S319

IJEAT Journal — Wed, 15 Mar 2023 11:11:25 +0000

Smart Helmet: Thresh-Learner–Online Machine Learning on Data Streams
Dhruv Garg¹, Aditya Chitlangia², Vetrivelan P³
¹Dhruv Garg, Department of Computer Science, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
²Aditya Chitlangia, Department of Computer Science, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
³Vetrivelan P, Department of Electronics and Communications, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 466-473 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10851291S319/19©BEIESP | DOI: 10.35940/ijeat.A1085.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: with an enormous generation and availability of time series data and streaming data, there is an increasing need for an automatic analyzing architecture to get fast interpretations and results. One of the significant potentiality of streaming analytics is to train and model each stream with unsupervised Machine Learning (ML) algorithms to detect anomalous behaviors, fuzzy patterns, and accidents in real-time. If executed reliably, each anomaly detection can be highly valuable for the application. In this paper, we propose a dynamic threshold setting system denoted as Thresh-Learner, mainly for the Internet of Things (IoT) applications that require anomaly detection. The proposed model enables a wide range of real-life applications where there is a necessity to set up a dynamic threshold over the streaming data to avoid anomalies, accidents or sending alerts to distant monitoring stations. We took the major problem of anomalies and accidents in coal mines due to coal fires and explosions. This results in loss of life due to the lack of automated alarming systems. We propose Thresh-Learner, a general purpose implementation for setting dynamic thresholds. We illustrate it through the Smart Helmet for coal mine workers which seamlessly integrates monitoring, analyzing and dynamic thresholds using IoT and analysis on the cloud.
Keywords: Anomaly Detection, Internet of Things, Online Machine Learning, Stream Processing.
Scope of the Article: Machine Learning

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A10201291S319

IJEAT Journal — Wed, 15 Mar 2023 11:02:55 +0000

Singularity Analysis of 3 DOF Parallel Manipulators for Angular Drilling in Medical Applications
Arockia Selva Kumar A
Dr. A. Arockia Selva Kumar, Senior Associate Professor, Design and Automation Research Group, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 461-465 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10201291S319/19©BEIESP | DOI: 10.35940/ijeat.A1020.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper focuses on two 3 – PRS (Prismatic – Revolute – Spherical) parallel manipulators (PMs) carried out by considering three type of singularities. For the study, pin joint forces and torques, linear displacement of nut and mobile platform (MP) tilt of TRIPOD and TRI-GLIDE PMs are taken as important parameters. The singularity positions of the parallel manipulators are simulated in ADAMS environment. The geometrical parameters of the parallel manipulators are considered in logical approach. Based on the results, the concluding remarks are summarized at the end to design the 3 – PRS parallel manipulators with better singular positions for angular drilling in medical applications.
Keywords: ADAMS, Parallel Manipulator, Singularity, TRIPOD, TRI-GLIDE.
Scope of the Article: Network Based Applications

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A10831291S319

IJEAT Journal — Wed, 15 Mar 2023 10:54:10 +0000

Low Power Implementation of Ternary Content Addressable Memory (TCAM)
Prithwiraj Das¹, Ria Pathak², P. Augusta Sophy Beulet³
¹Prithwiraj Das, Department of Electronics Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
²Ria Pathak, Department of Electronics Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
³Augusta Sophy Beulet P, Department of Electronics Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 455-460 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10831291S319/19©BEIESP | DOI: 10.35940/ijeat.A1083.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In network routers, Ternary Content Addressable Memory (TCAM)[1] based search engines take an important role. One of the improved versions of Content Addressable Memory (CAM) is TCAM. For high speed and broader searching operation TCAM is used. Unlike normal CAM, TCAM has 3 logic states: 0, 1, ‘X’. In TCAM within one single clock cycle, search operation can be performed. That is why it is called special type of memory. Also, quick search ability is one of the popular features of TCAM. To compare the search and stored data, TCAM array acts parallel in every location. But high power dissipation is the main disadvantage of TCAM. To overcome this power dissipation in this paper we proposed a low power TCAM implementation by using Reversible logic.[2] Reversible logic has less heat dissipating characteristics property with respect to irreversible gate. Also, Reversible logic has ultra-low power characteristics feature. In recent past it has been proved that reversible gates can implement any Boolean function.
Keywords: TCAM, CAM, Reversible Logic, Fredkin Gate, Feynman Gate, Peres Gate, Taffoli Gate.
Scope of the Article: Low-power design

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A10811291S319

IJEAT Journal — Wed, 15 Mar 2023 10:46:23 +0000

Iov Hybrid Architecture – Inter-Vehicular Communication and Data Transmission
Ishan Yash¹, Samir Ahmed Talkal², Karapurkar Shivani Prashant³
¹Ishan Yash, Department of Electronics and Communications Engineering, Specialization IoT Sensors, VIT Vellore (Tamil Nadu), India.
²Samir Ahmed Talkal, Department of Information Technology Engineering, VIT Vellore (Tamil Nadu), India.
³Karapurkar Shivani Prashant, Department of Electrical and Electronics Engineering, VIT Vellore (Tamil Nadu), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 447-454 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10811291S319/19©BEIESP | DOI: 10.35940/ijeat.A1081.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The Internet of Vehicle has an Intelligent Vehicular Grid consisting of the sensor platform, which continuously processes the data gathered from the surrounding of the vehicle and feeds it to the infrastructure. This data is further utilized by other vehicles to assist in traffic management, vehicle localization, pollution control and safe navigation. In this paper, we have suggested the ways through which we could attain a high data rate, seamless connectivity, security, improved quality of service and scalability. The first section of the paper consists of the proposed architecture and its benefits, and the latter consists of the scope of improvement with results using sensor dataset its application and challenges. Communication between the infrastructure and other vehicles can create privacy and security violations; hence, we also address ways to ensure the location privacy of the user.
Keywords: IoV (Internet of Vehicles), VANET (Vehicular ad-hoc Network), V2X, IoT (Internet of Things), SVM (Support Vector Machine), RAU (Radio Access unit).
Scope of the Article: Wireless Communications

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A10801291S319

IJEAT Journal — Wed, 15 Mar 2023 10:37:42 +0000

Low Cost Automated Washing Machine
Shanta Kumar B. Patil¹, Premjyoti Patil², Sreerajavenkatareddy Velagala³, Harshini Eggoni⁴
¹Shantakumar B. Patil, Department of Computer Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore (Karnataka), India.
²Premjyoti Patil, Department of Electronics & Communication Engineering, Nagarjuna college of Engineering and Technology, Bangalore (Karnataka), India.
³Sreerajavenkatareddy Velagala, Department of Computer Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore (Karnataka), India.
⁴Harshini Eggoni, Department of Computer Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore (Karnataka), India.
Manuscript received on 18 December 2019 | Revised Manuscript received on 24 December 2019 | Manuscript Published on 31 December 2019 | PP: 444-446 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10801291S319/19©BEIESP | DOI: 10.35940/ijeat.A1080.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Before purchasing any equipment, everybody will ask two things about the products. i. The cost of the equipment’s. ii. Efficiency of the products, both never go together. But in our proposed system we present such a system which fulfill both the conditions (low cost and efficient). Presently all hostess (House Wife) is using a smart phone. By making use of these smart phones we are controlling low cost non-automated washing machine and we use it like smart washing machine. The designed circuit used in the washing machine is simple and it consumes less power which in turn dissipate less heat and consumes less space.
Keywords: Smart Phone, Washing Machine.
Scope of the Article: Machine Learning

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