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[www.ijecs.in\] vol 5-issue 5 by Parnasree Chakraborty

— Block Compressive sensing technique has been proposed to exploit the sparse nature of medical i... more — Block Compressive sensing technique has been proposed to exploit the sparse nature of medical images in a transform domain to reduce the storage space. Block based compressive sensing is applied to dicom image, where original dicom image is divided in terms of blocks and each block is processed separately. The main advantage of block compressive sensing is that each block is processed independently and combined with parallel processing to reduce the amount of time required for processing. Compressed sensing exploits the sparse nature of images to reduce the volume of the data required for storage purpose. Inspired by this, we propose a new algorithm for image compression that combines compressed sensing with different transforms. Different sparse basis like discrete cosine transform, discrete wavelet transform and contourlet are used to compress the original input image. Among these transforms, Dct transform has block artifacts problem [14]. Wavelet transform can overcome the block artifacts introduced in the reconstructed image. Contourlet transform effectively captures smooth contours[4] and hence Contourlet transform provides better reconstruction quality image. In order to reconstruct original image, different techniques such as basis pursuit, orthogonal matching pursuit etc. are used at the decoder.

Papers by Parnasree Chakraborty

Measuring and monitoring Blood Pressure for cardiovascular patients is critical and essential too... more Measuring and monitoring Blood Pressure for cardiovascular patients is critical and essential too. A variation in heart rate is very important for a patient’s physiological condition analysis. But using cuff every time for measuring Blood Pressure, passing electrical signal to monitor Heart rate may irritate the patient. This idea provides cuff less measurement of Blood Pressure and Heart rate measurement using Photoplethysmography only. Arterial Blood Pressure and Photoplethysmogram along with Electrocardiogram is the most popular methods of measuring Cardio Vascular Status these days. Mostly both ABP and PPG together perform operations like monitoring Blood pressure with high efficiency and ECG for Heart rate. Measuring Heart rate from ECG and Blood Pressure from ABP may cause discomfort to the subject. So, we implement only PPG based system to monitor Both Blood Pressure and Heart rate. Now a days, Internet of Things, Cloud, Artificial Intelligence, and Machine Learning are takin...

Algorithms for intelligent systems, 2022

2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS)

Journal of The Institution of Engineers : Series B, 2021

With rapid development of real-time and dynamic applications, Compressed Sensing (CS) has been us... more With rapid development of real-time and dynamic applications, Compressed Sensing (CS) has been used for signal and image compression in the last decades. Storing the medical data and images remains a critical task for the health care sectors owing to the large storage needs. An extended applications of CS algorithm are suggested here for compression of biomedical signals and images for minimizing the storage space without compromising the quality. Electroencephalogram (EEG) signals and Digital Imaging and Communications in Medicine (DICOM) images are considered as medical signal and image as sample data for this proposed work. EEG signals using 16 different electrodes are collected from medical center and combined into a unique composite signal based on their statistical properties and then the composite signal is used as an input to the CS algorithm. The composite signal is converted into frequency domain for calculation of relative power in different frequency bands for identifica...

International Journal Of Engineering And Computer Science, 2016

Block Compressive sensing technique has been proposed to exploit the sparse nature of medical ima... more Block Compressive sensing technique has been proposed to exploit the sparse nature of medical images in a transform domain to reduce the storage space. Block based compressive sensing is applied to dicom image, where original dicom image is divided in terms of blocks and each block is processed separately. The main advantage of block compressive sensing is that each block is processed independently and combined with parallel processing to reduce the amount of time required for processing. Compressed sensing exploits the sparse nature of images to reduce the volume of the data required for storage purpose. Inspired by this, we propose a new algorithm for image compression that combines compressed sensing with different transforms. Different sparse basis like discrete cosine transform, discrete wavelet transform and contourlet are used to compress the original input image. Among these transforms, Dct transform has block artifacts problem [14]. Wavelet transform can overcome the block artifacts introduced in the reconstructed image. Contourlet transform effectively captures smooth contours[4] and hence Contourlet transform provides better reconstruction quality image. In order to reconstruct original image, different techniques such as basis pursuit, orthogonal matching pursuit etc. are used at the decoder.

2020 International Conference on Communication and Signal Processing (ICCSP), 2020

The learning ability and mental development assessment in young adult has got lot of importance i... more The learning ability and mental development assessment in young adult has got lot of importance in recent days. Many government and private organizations have come forward to analyze the strength and weakness of the programmes offered in education systems and hence it is essential to find some effective tools for determination of concentration level in young adult which in turn facilitate the trainer to assess the learning ability in adult. This paper proposes a unique method to identify the neurocognitive level based on EEG signal. The main objective of the proposed technique is to fill the gap in outcome based learning system by assessing the level of mental alertness of learner.

Data compression in Wireless Sensor Network (WSN) intends to lessen the energy consumption of the... more Data compression in Wireless Sensor Network (WSN) intends to lessen the energy consumption of the system. Wireless sensor network consists of many sensor nodes .A sensor node is used to acquire data from external environment. The Acquired data is wirelessly transmitted to the gateway node, where the data can be processed to reduce the dimension of data. Sensor nodes consume more power consumption during transmission, so battery should be replaced regularly .One of the method proposed in literature to prolong the battery life of the sensors is Compressive Sensing (CS) technique. Compressive sensing is a data compression technique that is used to represent the signals in a sparse domain. In this paper, real time data like temperature data and ECG data are acquired and compressed using CS algorithm and the proposed algorithm is implemented in hardware using WSN module. For the mentioned input data, around 75% data savings is achieved during transmission in WSN and hence battery power i...

In this paper, a hybrid data compression scheme based on predictive compressed sensing (CS) and l... more In this paper, a hybrid data compression scheme based on predictive compressed sensing (CS) and light weight lossless compression is suggested for wireless sensor networks (WSNs). CS based techniques are well motivated in WSNs not only for sparse signals but also by the requirement of efficient in-network processing in terms of transmit power and communication bandwidth even with nonsparse signals. This algorithm exploits prediction-based approach in which the difference between the actual measurements and the predicted measurements of the dataset is encoded using CS technique with reasonable error. The CS encoded data is further compressed using Huffman encoding to improve the compression ratio without any loss in quality. We analyzed the performance, data rate saving and inaccuracy introduced by the hybrid compression algorithm. The post processing analysis shows high compression ratios, with acceptable mean squared error.

Wireless sensor networks (WSNs) are typically resource constrained network due to restricted para... more Wireless sensor networks (WSNs) are typically resource constrained network due to restricted parameters like power supply, processing speed, memory requirement and bandwidth required for communication. Energy consumption is a key issue in the design of protocols and algorithms for WSNs due to their limited power supply. WSN operations involve sensing of data, computation, switching from node to node, transmission etc. In all these operations, energy efficiency is very essential. It is found in literature that, most of the energy is consumed in WSNs is due to the radio communications. In radio communication if the number of bits of data to be transmitted is reduced by some amount then it is possible to reduce the energy consumption. Hence it is essential to use data compression to reduce the number of bits to be transmitted. Researchers have investigated many energy efficient light weight compression algorithms suitable for WSN data. Still there is a requirement for efficient compression algorithms for WSN which minimizes the mean square error (MSE) of received data and hence in this paper differential encoding based compressed sensing (CS) algorithm is suggested. A CODEC design is suggested for improving the reconstruction quality. Simulation results show improvement in reconstruction quality and reduction in MSE value compared to standard compressed sensing technique.

Wireless Personal Communications

Futuristic Communication and Network Technologies

American Journal of Applied Sciences

Wireless Sensor Networks (WSN) are comprised of spatially distributed sensor nodes, where each no... more Wireless Sensor Networks (WSN) are comprised of spatially distributed sensor nodes, where each node contains sensors, processors and transceivers for communicating data. Regardless of the application in which the sensor network is serving, the data generated in the network eventually must be delivered to the sink. However the limited network bandwidth, frequent node/link failure along with the unreliable communication medium poses great challenges for node to node communication in WSN. Hence, energy efficient data compression algorithms are necessary for sensor nodes as they enhance the transmission efficiency in WSN. Compressive sensing is a new compression algorithm in which the input signal is converted into sparse signal and the sparse signal is further converted into a signal of reduded dimension than original signal. The dimensionality reduction improves the transmission efficiency. This new concept is recently applied in WSN, however suitable threshold selection to sparsify the one dimensional sensor reading and suitable sparifying basis for image input data are not considered in literature. Hence, in this paper analysis of compressive sensing algorithm with a suitable threshold selection is performed in order to increase the level of sparsity for one dimensional data and a suitable sparsifying basis selection is performed for image data. Results indicate that compressive sensing with suitable threshold selection improves transmission and bandwidth efficiency in case of low correlated one dimensional sensor data and a suitable basis improves the quality of transmission for image sensor data and hence the overall lifetime of sensor network can be increased.

IOSR Journal of Electronics and Communication Engineering, 2014