iosr-JVSP   Volume-15 ~ Issue-5 ~ Sep - Oct 2025

ABSTRACT: This document presents two distinct approaches to compressive sensing for the acquisition and reconstruction of non-sparse signals. The first approach directly compresses the original signal using a measurement matrix, while the second approach first transforms the signal into a sparse representation before compression. The reconstruction phases employ the Orthogonal Matching Pursuit (OMP) algorithm to solve the minimization problem. The quality of reconstruction is evaluated using the Mean Squared Error (MSE), with both visual and numerical comparisons for different measurement sizes......

Key Word: Compressive sensing, sparse signal, Orthogonal Matching Pursuit, Mean Squared Error

[1] David L. Donoho, « Compressed Sensing », IEEE Transactions On Information Theory, Vol. 52, No. 4, Pp. 1289–1306, 2006
[2] E. J. Candès, J. Romberg, And T. Tao, « Stable Signal Recovery From Incomplete And Inaccurate Measurements », Communications On Pure And Applied Mathematics, Vol. 59, No. 8, Pp. 1207–1223, 2006
[3] Vishal M. Patel And Rama Chellappa, « Sparse Representations, Compressive Sensing And Dictionaries For Pattern Recognition », In 1st Asian Conference On Pattern Recognition (ACPR 2011), 2011
[4] C. A. Wong, « Can An Underdetermined System Have A Unique Solution? », Mathematics Stack Exchange, 2012
[5] J. Laska And R. G. Baraniuk, « Regime Change: Bit-Rate Reduction And Mismatch In Compressed Sensing », IEEE Signal Processing Letters, Vol. 14, No. 8, Pp. 521–524, 2007.

ABSTRACT: The Key Objective is HDL RTL Design Architecture of Ultra high multi Clock Frequency Speed Rate ( MHz, GHz, THz, PHz, EHz, ZHz, etc) Bits Per Second Baud Rate ) P.R.B.S(Pseudo Random Binary Sequence) Transceiver Soft A.S.I.C IP Core product for identification of the property of Different Pseudo Random Binary Sequence Patterns (Seed Words) of 2e7-1, 2e10-1, 2e15-1, 2e23-1, 2e31-1 tapped elements as per C.C.I.T.T-I.T.U Standards and IEEE-754 Single and Double Data Rate Data Precision Standards (32 bit & 64 Bit Data Width ) suited for Very.....

Key Word: P.R.B.S- Pseudo Random Binary Sequence, Wi-Fi-Wireless Fidelity, F.P.G.A – Field Programmable Gate Array, IDE – Integrated Development Environment. C.C.I.T.T- Consulting Committee for International Telegraph and Telephone, I.T.U- International Telecommunication Union, A.S.I.C- Application Specific Integrated Circuit, E.D.A- Electronic Design Automation

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ABSTRACT: Agricultural productivity is an important factor in the Indian economy. Therefore, the contribution of food and cash crops is very important to both the environment and people. Each year, crops succumb to several diseases. The diagnosis of such diseases is inadequate, and many plants die due to ignorance of the symptoms of the disease and its treatment. This is done using image processing techniques. A total of 15 cases were fed to the model, 12 of which were Bell Paper Bacterial Spot, Potato Early Bright, Potato Rate Bright, Tomato Target Spot, Tomato Mosaic......

Key Word: Convolutional Neural Network (CNN), Leaf Disease, etc.

[1]. Sardogan, M., Tuncer, A., and Ozen, Y.: Plant Leaf Disease Detection and ClassificationBasedonCNNwiththeLVQAlgorithm.In:3rdInt.Conf.Comput.Sci.Eng.(2018)382–385
[2]. Wallelign, S., Polceanu, M., and Buche, C.: Soybean plant disease identification using aconvolutionalneuralnetwork.In:Proc.31stInt.FloridaArtif.Intell.Res.Soc.Conf.
[3]. FLAIRS2018(2018),146–151
[4]. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., and Stefanovic, D.: Deep NeuralNetworks Based Recognition of Plant Diseases by Leaf Image Classification. Comput.Intell.Neurosci. 2016(2016)
[5]. Fuentes, A., Yoon, S., Kim, S. C., and Park, D. S.: A robust deep-learning-based detectorforreal-timetomatoplantdiseasesandpestsrecognition.Sensors(Switzerland)17(2017)..

ABSTRACT: Irregular heartbeats due to abnormal electrical heart activity are symptom of Cardiovascular disease (CVD), it is a source of stroke, blood clots, heart failure and other heart-related complications. Most of the developed Electrocardiogram (ECG) based automatic cardiac arrhythmia detection systems require the availability of a large data with all arrhythmias for the training process, and cannot be updated without adequate data and cost. Therefore......

Key Word: Electrocardiogram classification; continual learning; catastrophic forgetting; generative model, contrastive learning.

[1]. A. Sahu, H. GM, M. K. Gourisaria, S. S. Rautaray, and M. Pandey, “Cardiovascular risk assessment using data mining inferencing and feature engineering techniques,” International Journal of Information Technology, vol. 13, no. 5, pp. 2011–2023, 2021.
[2]. American Heart Association, “Heart disease, stroke and research statistics at-a-glance,” Retrieved on April, vol. 1, p. 2016, 2016.
[3]. S. Mendis, P. Puska, B. Norrving, and World Health Organization, Global atlas on cardiovascular disease prevention and control. World Health Organization, 2011.
[4]. R. Mehra, “Global public health problem of sudden cardiac death,” Journal of electrocardiology, vol. 40, no. 6, pp. S118–S122, 2007.
[5]. C. Ye, M. T. Coimbra, and B. V. Kumar, “Arrhythmia detection and classification using morphological and dynamic features of ECG signals,” 2010, pp. 1918–1921

ABSTRACT: Convolution coding has been used in communication systems including deep space communication and wireless communication. With demand for higher data rates beyond 10Gbps as per the recommendations of 60GHz communication standards processing speed of hardware and software modules need to further enhance to meet the requirements. VLSI circuits are designed with transistors sizing in nanometer range so as to operate at higher frequencies......

Key Word: Ternary computing, Ternary convolutional encoder , Higher frequency .

[1]. Fazal Noorbasha, G.Jhansi , K.Deepthi , K Hari Kishore, “ASIC Implementation of Convolution Encoder and Viterbi Decoder Based Cryptography System” (IJITEE) ISSN: 2278-3075, Volume-8 Issue-6S, April 2019.
[2]. Zhang Taotao, Zhang JingKe, Zhou Zhiwen, Yang Zhifei, Liu Wanhong, “FPGA-Based Large Constraint Length Convolution Code Encoder Verification, ICSP 2019
[3]. Dr. S Aruna, Mr. A.V. Adiseshu, Dr.K.Srinivasa Naik , “Design of Viterbi Decoder for Speech to Text Conversion Application using ACS Architecture,IOSR journal of VLSI and signal processing Jan 21, 2019
[4]. Gabriele Meoni, “Design Optimization for High Throughput Recursive Systematic Convolutional Encoders” ICSTCC 2018
[5]. Prof. Vijaya Bharathi M, Sneha.H., Mahesh.M., Shwetha.N., Sowmya.S, “Forward Error Correcting Implementation Using Convolutional Encoders and Viterbi decoding” International Journal of Electrical, Electronics and Computer Systems (IJEECS) Volume -6, Issue-3, 2018