deepthi aruna

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Papers by deepthi aruna

Analysis of Social Network Data Mining for Security Intelligence Privacy Machine Learning

International Journal on Recent and Innovation Trends in Computing and Communication, Aug 17, 2023

Analysis of nanowire FET over FinFET

PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON FRONTIER OF DIGITAL TECHNOLOGY TOWARDS A SUSTAINABLE SOCIETY

Applications of data analytics in placements using machine learning

COMPUTATIONAL INTELLIGENCE AND NETWORK SECURITY

Handwritten to Text Document Converter

Lecture notes in electrical engineering, 2022

Diabetic monitoring system using Internet of Everything

2022 IEEE International Symposium on Smart Electronic Systems (iSES)

Secure MRI Brain Image Transmission Using IOT Devices Based on Hybrid Autoencoder and Restricted Boltzmann Approach

Journal of Sensors, May 29, 2022

In recent times, the medical image processing solves several clinical issues by inspecting the vi... more In recent times, the medical image processing solves several clinical issues by inspecting the visual images, which are generated in the clinical health care units. The main objective of the research is to gain valuable information from the images for better clinical diagnosis. In the biomedical engineering domain, the medical image analysis is an emerging research topic. In the recent decades, the use of medical images is highly growing, which are acquired from different image modalities; so, there is a necessity for data compression for transmission, storage, and management of digital medical image datasets. Hence, the machine learning methods are effective for medical image analysis, where the deep learning models are used in the machine learning tools for automatically learning the feature vectors from the huge medical datasets. The automated deep learning models are effective compared to the conventional handcrafted features. In addition to this, a wireless sensor network (WSN) is used to create a primary health care scheme, which brings patient data together and expands the medical conveniences, whereas the WSN design must comprise of sensor nodes, because it consumes less power and resources at a relatively low cost; so, it is essential for implementing the Raspberry Pi-based WSN nodes. The sensor nodes are important for limited battery capacity and to transmit the vast amount of medical data. The proposed work is broadly classified into two categories such as (i) the medical image compression algorithm is developed using the deep learning model based on autoencoders and restricted Boltzmann machines (RBM) and (ii) implementation of the WSN sensors nodes with Raspberry Pi and Messaging Queue Telemetry Transport (MQTT) Internet of Things (IoT) protocol for secure transmission of the medical images. The experimental results are evaluated using the standard performance metrics like peak signal to noise ratio (PSNR) and presented a Real-Time Linux (RTL) implementation of the design. The proposed model showed 10 dB to 15 dB improvement in the PSNR value, while transmitting the medical images, which is better compared to the existing model.

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Human Activity Recognition Using Single Frame CNN

Lecture notes in electrical engineering, 2022

Image Transmission Using Leach and Security Using RSA in Wireless Sensor Networks

Computational Vision and Bio-Inspired Computing, 2022

Analysis of Performance of Switched Capacitor Circuits

In switched capacitor circuits, it is quite common to use MOSFET switches, integrated capacitors ... more In switched capacitor circuits, it is quite common to use MOSFET switches, integrated capacitors (IC) and non-overlapping clocks for carrying out switching operations. MOSFET switches are far from ideal switches mainly when they are operated with small operating voltages. Some investigations have been carried out on the performance of switched capacitor resistors (SCRs) with the switches operated under varied conditions. Further, an attempt has been made to find out how closely SCR represents conventional resistors. It has been found that the SCRs with ideal components satisfy the properties of series and parallel connected conventional resistors. Further, the energy dissipation in SCR is the same as that in conventional resistors when operated under fixed voltage difference. FET based SCRs exhibit higher resistance values, compared to SCRs with ideal switches depending on the operating conditions. When SCRs are loaded with conventional resistors or capacitors, their output is entir...

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Image transmission and compression techniques using SPIHT and EZW in WSN

Due to increasing demand for wireless sensor networks, many transmission and compression techniqu... more Due to increasing demand for wireless sensor networks, many transmission and compression techniques are formulated which are useful for day to day applications. This paper focuses mainly on the various compression techniques like SPIHT, EZW, DCT and different routing protocols like Ad Hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) routing protocols. The performance metrics of various compression algorithms like M.S.E and P.S.N.R and different parameters like delay, throughput, packet delivery ratio and end to end simulations of various routing algorithms are compared in WSN.

Image compression techniques in wireless sensor networks

2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 2017

There are various compression techniques like transform coding, Entropy coding, Arithmetic coding... more There are various compression techniques like transform coding, Entropy coding, Arithmetic coding. Wavelet based coding etc. In this paper an attempt is made to combine Haar and Hadamard transform. This algorithm is used to compress the images in lossless and lossy compressions. DCT (Discrete Cosine Transform), Hadamard along with Haar transform is used to compress the image and enhance the transmission in Wireless Sensor Networks. The enhancement in image quality and compression ratio is achieved. The compression is done by combining various compression algorithms like Haar and Hadamard transforms. Compression algorithms include Haar, DCT, Hadamard Quantization and Entropy encoding. The decompression is the inverse of the above process. Now, the compressed file is sent through Wireless Sensor Networks (WSN). The source node receives the file and is transmitted to destination through network parameters such as routing, energy, delay, packet delivery ratio (PDR), delay are calculated...

RTL Implementation of image compression techniques in WSN

International Journal of Electrical and Computer Engineering (IJECE)

The Wireless sensor networks have limitations regarding data redundancy, power and require high b... more The Wireless sensor networks have limitations regarding data redundancy, power and require high bandwidth when used for multimedia data. Image compression methods overcome these problems. Non-negative Matrix Factorization (NMF) method is useful in approximating high dimensional data where the data has non-negative components. Another method of the NMF called (PNMF) Projective Nonnegative Matrix Factorization is used for learning spatially localized visual patterns. Simulation results show the comparison between SVD, NMF, PNMF compression schemes. Compressed images are transmitted from base station to cluster head node and received from ordinary nodes. The station takes on the image restoration. Image quality, compression ratio, signal to noise ratio and energy consumption are the essential metrics measured for compression performance. In this paper, the compression methods are designed using Matlab.The parameters like PSNR, the total node energy consumption are calculated. RTL schematic of NMF SVD, PNMF methods is generated by using Verilog HDL.

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Image Compression in Wireless Sensor Networks Using Autoencoder and RBM Method

Topics in Heterocyclic Chemistry

A Review of Image Compression and Comparison of its Algorithms

Image compression is now essential for applications such as transmission and storage in data base... more Image compression is now essential for applications such as transmission and storage in data bases. In this paper we review and discuss about the image compression, need of compression, its principles, and classes of compression and various algorithm of image compression. This paper attempts to give a recipe for selecting one of the popular image compression algorithms based on Wavelet, JPEG/DCT, VQ, and Fractal approaches. We review and discuss the advantages and disadvantages of these algorithms for compressing grayscale images, give an experimental comparison on 256×256 commonly used image of Lenna and one 400×400 fingerprint image.

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