Electrocardiogram transmission over OFDM system

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Divya Jain ◽  
Sanjeev Narayan Sharma ◽  
Alok Jain
Keyword(s):  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Data Representation ◽  
Quantization Error ◽  
Remote Patient Monitoring ◽  
Structural Similarity Index ◽  
Remote Patient ◽  
Electrocardiogram Ecg

Abstract Remote patient monitoring is an important area of research. Electrocardiogram (ECG) is a vital health parameter which can be remotely transmitted to monitor the patient’s health. In this field, there are many research directions which include ECG security, patient data hiding in ECG, ECG classification, ECG transmission and reception. In this paper, an effective methodology for ECG transmission over OFDM wireless communication system has been presented. Issues related to transmission of ECG as image have also been discussed. Before ECG is transmitted over OFDM analog ECG signal is to be converted into series of bits for which sampling and quantization has to be performed. The quantization error arises due to number of chosen samples and quantization levels. In this work, it is shown that the quantization error can be brought down to zero using symbolic aggregate approximation (SAX) for data representation. Performance of the ECG transmission using different methodologies has been compared using peak signal-to-noise ratio (PSNR) Structural Similarity Index Metric (SSIM), Bit Error Rate (BER) and Pixel Error Rate (PER).

ANALISIS SENSITIVITAS VIDEO MPEG-4 BERDASARKAN STRUKTUR FRAME PADA TRANSMISI DVB-T

2020 ◽  
Vol 25 (2) ◽  
pp. 86-97
Author(s):  
Sandy Suryo Prayogo ◽  
Tubagus Maulana Kusuma
Keyword(s):  
Deep Learning ◽  
Bit Error Rate ◽  
Error Rate ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Signal To Noise ◽  
Structural Similarity Index ◽  
Noise Ratio

DVB merupakan standar transmisi televisi digital yang paling banyak digunakan saat ini. Unsur terpenting dari suatu proses transmisi adalah kualitas gambar dari video yang diterima setelah melalui proses transimisi tersebut. Banyak faktor yang dapat mempengaruhi kualitas dari suatu gambar, salah satunya adalah struktur frame dari video. Pada tulisan ini dilakukan pengujian sensitifitas video MPEG-4 berdasarkan struktur frame pada transmisi DVB-T. Pengujian dilakukan menggunakan simulasi matlab dan simulink. Digunakan juga ffmpeg untuk menyediakan format dan pengaturan video akan disimulasikan. Variabel yang diubah dari video adalah bitrate dan juga group-of-pictures (GOP), sedangkan variabel yang diubah dari transmisi DVB-T adalah signal-to-noise-ratio (SNR) pada kanal AWGN di antara pengirim (Tx) dan penerima (Rx). Hasil yang diperoleh dari percobaan berupa kualitas rata-rata gambar pada video yang diukur menggunakan metode pengukuran structural-similarity-index (SSIM). Dilakukan juga pengukuran terhadap jumlah bit-error-rate BER pada bitstream DVB-T. Percobaan yang dilakukan dapat menunjukkan seberapa besar sensitifitas bitrate dan GOP dari video pada transmisi DVB-T dengan kesimpulan semakin besar bitrate maka akan semakin buruk nilai kualitas gambarnya, dan semakin kecil nilai GOP maka akan semakin baik nilai kualitasnya. Penilitian diharapkan dapat dikembangkan menggunakan deep learning untuk memperoleh frame struktur yang tepat di kondisi-kondisi tertentu dalam proses transmisi televisi digital.

scholarly journals Lifting Wavelet Transform in Compressive Sensing for MRI Reconstruction

2020 ◽  
Vol 55 (5) ◽  
Author(s):  
Indrarini Dyah Irawati ◽  
Sugondo Hadiyoso ◽  
Gelar Budiman ◽  
Asep Mulyana
Keyword(s):  
Wavelet Transform ◽  
Magnetic Resonance ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Reconstruction Algorithm ◽  
Structural Similarity ◽  
Lifting Wavelet Transform ◽  
Structural Similarity Index ◽  
Compressed Sampling ◽  
Lifting Wavelet

Compressed sampling in the application of magnetic resonance imaging compression requires high accuracy when reconstructing from a small number of samples. Sparsity in magnetic resonance images is a fundamental requirement in compressed sampling. In this paper, we proposed the lifting wavelet transform sparsity technique by taking wavelet coefficients on the low pass sub-band that contains meaningful information. The application of novel methods useful for compressing data with the highest compression ratio at the sender but still maintaining high accuracy at the receiver. These wavelet coefficient values are arranged to form a sparse vector. We explore the performance of the proposed method by testing at several levels of lifting wavelet transform decomposition, include Levels 2, 3, 4, 5, and 6. The second requirement for compressed sampling is the acquisition technique. The data sampled sparse vectors using a normal distributed random measurement matrix. This matrix is normalized to the average energy of the image pixel block. The last compressed sampling requirement is a reconstruction algorithm. In this study, we analyze three reconstruction algorithms, namely Level 1 magic, iteratively reweighted least squares, and orthogonal matching pursuit, based on structural similarity index measured and peak signal to noise ratio metrics. Experimental results show that magnetic resonance imaging can be reconstructed with higher structural similarity index measured and peak signal to noise ratio using the lifting wavelet transform sparsity technique at a minimum decomposition level of 4. The proposed lifting wavelet transforms and Level 1 magic reconstruction algorithm has the best performance compared to the others at the measurement rate range between 10 to 70. This method also outperforms the techniques in previous studies.

scholarly journals Evaluation of objective video quality assessment methods on video sequences with different spatial and temporal activity encoded at different spatial resolutions

2021 ◽  
Vol 12 (1) ◽  
pp. 1-9
Author(s):  
Jelena Vlaović ◽  
Drago Žagar ◽  
Snježana Rimac-Drlje ◽  
Mario Vranješ
Keyword(s):  
Quality Assessment ◽  
Signal To Noise Ratio ◽  
Video Quality ◽  
Similarity Index ◽  
Structural Similarity ◽  
Video Sequences ◽  
Large Set ◽  
Signal To Noise ◽  
Structural Similarity Index

With the development of Video on Demand applications due to the availability of high-speed internet access, adaptive streaming algorithms have been developing and improving. The focus is on improving user’s Quality of Experience (QoE) and taking it into account as one of the parameters for the adaptation algorithm. Users often experience changing network conditions, so the goal is to ensure stable video playback with satisfying QoE level. Although subjective Video Quality Assessment (VQA) methods provide more accurate results regarding user’s QoE, objective VQA methods cost less and are less time-consuming. In this article, nine different objective VQA methods are compared on a large set of video sequences with various spatial and temporal activities. VQA methods used in this analysis are: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), MultiScale Structural Similarity Index (MS-SSIM), Video Quality Metric (VQM), Mean Sum of Differences (DELTA), Mean Sum of Absolute Differences (MSAD), Mean Squared Error (MSE), Netflix Video Multimethod Assessment Fusion (Netflix VMAF) and Visual Signal-to-Noise Ratio (VSNR). The video sequences used for testing purposes were encoded according to H.264/AVC with twelve different target coding bitrates, at three different spatial resolutions (resulting in a total of 190 sequences). In addition to objective quality assessment, subjective quality assessment was performed for these sequences. All results acquired by objective VQA methods have been compared with subjective Mean Opinion Score (MOS) results using Pearson Linear Correlation Coefficient (PLCC). Measurement results obtained on a large set of video sequences with different spatial resolutions show that VQA methods like SSIM and VQM correlate better with MOS results compared to PSNR, SSIM, VSNR, DELTA, MSE, VMAF and MSAD. However, the PLCC results for SSIM and VQM are too low (0.7799 and 0.7734, respectively), for the usage of these methods in streaming services instead of subjective testing. These results suggest that more efficient VQA methods should be developed to be used in streaming testing procedures as well as to support the video segmentation process. Furthermore, when comparing results obtained for different spatial resolutions, it can be concluded that the quality of video sequences encoded at lower spatial resolutions in cases of lower target coding bitrate is higher compared to the quality of video sequences encoded at higher spatial resolutions at the same target coding bitrate, particularly when video sequences with higher spatial and temporal information are used.

scholarly journals Multi-wavelet level comparison on compressive sensing for MRI image reconstruction

2020 ◽  
Vol 9 (4) ◽  
pp. 1461-1467
Author(s):  
Indrarini Dyah Irawati ◽  
Sugondo Hadiyoso ◽  
Yuli Sun Hariyani
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Gaussian Random Process ◽  
Structural Similarity Index ◽  
Level 3 ◽  
Mri Reconstruction ◽  
Multi Level ◽  
Index Measure ◽  
Level 2

In this study, we proposed compressive sampling for MRI reconstruction based on sparse representation using multi-wavelet transformation. Comparing the performance of wavelet decomposition level, which are Level 1, Level 2, Level 3, and Level 4. We used gaussian random process to generate measurement matrix. The algorithm used to reconstruct the image is . The experimental results showed that the use of wavelet multi-level can generate higher compression ratio but requires a longer processing time. MRI reconstruction results based on the parameters of the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) show that the higher the level of decomposition in wavelets, the value of both decreases.

scholarly journals On the Implementation of Multi-bit Inexact Adder Cells and Application towards Image De-noising

2020 ◽  
Vol 24 (1) ◽  
Author(s):  
Srikant Kumar Beura ◽  
Amol Arjun Jawale ◽  
Bishnulatpam Pushpa Devi ◽  
Prabir Saha
Keyword(s):  
Signal To Noise Ratio ◽  
Substantial Reduction ◽  
Similarity Index ◽  
Digital Signal ◽  
Structural Similarity ◽  
Propagation Delay ◽  
Substantial Improvement ◽  
Structural Similarity Index ◽  
Error Metrics ◽  
And Performance

Inexact computing is an attractive concept for digital signal processing at the submicron regime. This paper proposes 2-bit inexact adder cell and further escalate to 4-bit, and 8-bit inexact adder and error metrics have been evaluated mathematically for such adder cells. The approximated design has been proposed through the simplification of the K-Maps, which leads to a substantial reduction in the propagation delay as well as energy consumption. The proposed design has been verified through the Cadence Spectre and performance parameters (such as delay, power consumption) have been evaluated through CMOS gpdk45 nm technology. Furthermore, the proposed design has been applied to image de-noising application where the performance of the images like Peak Signal to Noise Ratio (PSNR), Normalized Correlation Coefficient (NCC) and Structural Similarity Index (SSIM) has been analyzed through MATLAB, which offer the substantial improvement from its counterpart.

scholarly journals Medical Image Watermarking Technique Using Image Interpolation in Transform Domain

2018 ◽  
Vol 7 (3.12) ◽  
pp. 711
Author(s):  
S Priya ◽  
B Santhi ◽  
J Raja Mohan
Keyword(s):  
Medical Image ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Absolute Error ◽  
Medical Data ◽  
Experimental Result ◽  
Structural Similarity Index ◽  
Medical Image Watermarking

In telemedicine, medical data are shared across the world among different specialists for various purposes through an unsecured medium. So there is a need to protect the medical data during transmission. With the help of image watermarking techniques, medical images are protected along with the electronic patient information (EPI). This paper proposes a medical image watermarking, by applying wavelet transform, using an interpolation technique. EPI data is embedded within the transformed medical image to generate a watermarked image. At the extraction side, EPI data are extracted and medical image is reconstructed without any loss. The performance of the proposed method is analyzed using a peak signal to noise ratio (PSNR), mean absolute error (MAE) and structural similarity index (SSIM).   The experimental result shows that the proposed method gives better results.

scholarly journals Digital Image Steganography Scheme Based on DWT and SVD

2020 ◽  
Vol 13 (4) ◽  
pp. 10-17
Author(s):  
Fadhil Kadhim Zaidan
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Image Steganography ◽  
Discrete Wavelet ◽  
Grayscale Image ◽  
Structural Similarity Index ◽  
Value Decomposition ◽  
Index Measure

In this work, a grayscale image steganography scheme is proposed using a discrete wavelet transform (DWT) and singular value decomposition (SVD). In this scheme, 2-level DWT is applied to a cover image to obtain the high frequency band HL2 which is utilized to embed a secret grayscale image based on the SVD technique. The robustness and the imperceptibility of the proposed steganography algorithm are controlled by a scaling factor for obtaining an acceptable trade-off between them. Peak signal to noise ratio (PSNR) and Structural Similarity Index Measure (SSIM) are used for assessing the efficiency of the proposed approach. Experimental results demonstrate that the proposed scheme still holds its validity under different known attacks such as noise addition, filtering, cropping and JPEG compression

scholarly journals Removing the Traces of Median Filtering via Unsharp Masking as an Anti-forensic Approach in Medical Imaging

2019 ◽  
Vol 12 (3) ◽  
pp. 1395-1402 ◽  
Author(s):  
Athira B kaimal ◽  
Priestly Shan B
Keyword(s):  
Signal To Noise Ratio ◽  
Median Filter ◽  
Similarity Index ◽  
Structural Similarity ◽  
Median Filtering ◽  
Fast Computation ◽  
Structural Similarity Index ◽  
Unsharp Masking ◽  
Objective Function Values ◽  
Forensic Tools

Development of post-processing algorithms which cannot be detected by forensic tools is an active area of research in image processing. Median Filter (MF) is one among the denoising schemes which is specifically targeted by the forensic toolsbecause of its wide application in commercial raster graphic editors, simplicity, fast computation and detail preserving characteristics. Methodsbased on Convolutional Neural Networks (CNN) and Variational Deconvolution (VD), meant for reducing the forensic detectability of MF by removing the traces of filtering from the output images are computationally intense. A simple and computationally feasible approach for removing the traces of median filtering from the output images, thereby to reduce the forensic detectability of MF is proposed in this paper. In the proposed approach, blurred edges in the output of MF are restored with the help of Unsharp Masking (UM). Optimum value of the amount which controls the degree of sharpening in the UM algorithm is determined via minimum error sense criterion by making use of Peak Signal to Noise Ratio (PSNR) between input and processed images as objective function. Values of PSNR and Structural Similarity Index Metric (SSIM) between input and output images exhibited by the proposed algorithm are found to be higher than those exhibited by methods based on CNN, VD and combined framework of VD and Total Variation (TV) minimisation.

scholarly journals Comparative analysis between non-linear wavelet based image denoising techniques

2018 ◽  
Vol 5 ◽  
pp. 58-67
Author(s):  
Milan Chikanbanjar
Keyword(s):  
Visual Information ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Hard Thresholding ◽  
Structural Similarity Index ◽  
Thresholding Method ◽  
Non Linear ◽  
Good Quality Image

Digital images have been a major form of transmission of visual information, but due to the presence of noise, the image gets corrupted. Thus, processing of the received image needs to be done before being used in an application. Denoising of image involves data manipulation to remove noise in order to produce a good quality image retaining different details. Quantitative measures have been used to show the improvement in the quality of the restored image by the use of various thresholding techniques by the use of parameters mainly, MSE (Mean Square Error), PSNR (Peak-Signal-to-Noise-Ratio) and SSIM (Structural Similarity index). Here, non-linear wavelet transform denoising techniques of natural images are studied, analyzed and compared using thresholding techniques such as soft, hard, semi-soft, LevelShrink, SUREShrink, VisuShrink and BayesShrink. On most of the tests, PSNR and SSIM values for LevelShrink Hard thresholding method is higher as compared to other thresholding methods. For instance, from tests PSNR and SSIM values of lena image for VISUShrink Hard, VISUShrink Soft, VISUShrink Semi Soft, LevelShrink Hard, LevelShrink Soft, LevelShrink Semi Soft, SUREShrink, BayesShrink thresholding methods at the variance of 10 are 23.82, 16.51, 23.25, 24.48, 23.25, 20.67, 23.42, 23.14 and 0.28, 0.28, 0.28, 0.29, 0.22, 0.25, 0.16 respectively which shows that the PSNR and SSIM values for LevelShrink Hard thresholding method is higher as compared to other thresholding methods, and so on. Thus, it can be stated that the performance of LevelShrink Hard thresholding method is better on most of tests.

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