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"refining image Quality: Deep Neural Network applications in Plane Wave Medical Imaging" 7

"refining Image Quality: Deep Neural Network Applications in...

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7th International conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Deva Shahila, D. Ferlin Suresh, L. Padma Ida, A. Merry Evangelin, D. Lilly Jeyanthy, P Aruna Stephen, Valantina Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of ECE Chennai India Baselios Mathews II College of Engineering Kollam Department of CSE Chennai India Loyola Institute of Technology and Science Loyola Nagar Thovalai Department of CSE Kanya Kumari India Loyola Institute of Technology and Science Loyola Nagar Thovalai Department of CSE Chennai India Loyola Institute of Technology and Science Loyola Nagar Thovalai Department of EEE Kanya Kumari India Kalasalingam Academy of Research and Education Department of EEE Tamil Nadu Srivilliputhur India Saveetha School of Engineering Thandalam Department of EEE Chennai India

ISBN: (纸本)9798350372816

With the goal to attain a high frame rate, plane wave imaging (PWI), a common swift ultrasonography imaging in medicine method, uses a solitary plane wave emission with no focal point. However, as compared to the widely utilized targeted line scan mode, the imaging quality is significantly reduced. Imaging performance can be enhanced with conventional adaptive beamformers, albeit at the expense of more processing. In the present study, we suggest employing a deep neural network (DNN) to maintain a high frame rate despite improving PWI performance. Specifically, the key contribution of this technology is The network input is the PWI response from a single point target, and the narrower scan response from the same point appears as the desired output. An amalgam of phantom investigations, in vivo research, and computations are used to assess the effectiveness of the suggested approach. For comparison coherence factor (CF), a prior anticipated deep learning-based technique, the delay-and-sum (DAS), and the DAS with a customized scan are employed. The performance is measured using numerical metrics, such as the speckle the contrast ratio (CR), signal-to-noise ratio, and contrast-to-noise ratio (CNR) (SSNR). The outcomes show that the suggested strategy is capable of achieving better contrast and resolution. In particular, the suggested approach outperforms the DAS across the board. While the CF offers a greater CR, the suggested The SSNR and CNR of this strategy are significantly greater. In addition, the total performance is comparable with concentrated scan performance and superior to the conventional deep learning technique. Furthermore, the suggested technique ensures good temporal resolution with less additional computation needed comparison to the DAS. The aforementioned results confirm that the suggested strategy is capable of producing high-quality images while preserving the high frame rate required for PWI. © 2024 IEEE.

关键词： Medical imaging

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A unified approach to fractal dimensions

A unified approach to fractal dimensions

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IEEE International conference on Cognitive Informatics

作者： W. Kinsner Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

Many scientific papers treat the diversity of fractal dimensions as mere variations on either the same theme or a single definition. There is a need for a unified approach to fractal dimensions for there are fundamental differences between their definitions. This paper presents a new description of three essential classes of fractal dimensions based on: (i) morphology, (ii) entropy, and (iii) transforms, all unified through the generalized entropy-based Renyi fractal dimension spectrum. It discusses practical algorithms for computing 15 different fractal dimensions representing the classes. Although the individual dimensions have already been described in the literature, the unified approach presented in this paper is unique in terms of (i) its progressive development of the fractal dimension concept, (ii) similarity in the definitions and expressions, (iii) analysis of the relation between the dimensions, and (iv) their taxonomy. As a result, a number of new observations have been made, and new applications discovered. Of particular interest are behavioural processes such as dishabituation, irreversible and birth-death growth phenomena e.g., diffusion-limited aggregates, DLAs, dielectric discharges, and cellular automata, as well as dynamical nonstationary transient processes such as speech and transients in radio transmitters, multifractal optimization of image compression using learned vector quantization with Kohonen 's self-organizing feature maps (SOFMs), and multifractal-based signal denoising.

关键词： Fractals Morphology Entropy Taxonomy Aggregates Dielectrics Speech processing Radio transmitters image coding Vector quantization

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Design and Implementation of 111 MHz Frequency Compression Efficient CORDIC Based 2D-DCT using FPGA and its Power Performance

Design and Implementation of 111 MHz Frequency Compression E...

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IEEE Bombay Section Signature conference (IBSSC)

作者： Bharati Y. Masram P. T. Karule Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9781665417594

The new idea behind the CORDIC algorithm used for contemporary DSP applications is introduced in this work due to a very appealing and simple technique. This method not only reduces latency but also improves the throughput of output parameters and also reduces the complexity of 2D –DCT computations. This paper describes an FPGA-based CORDIC processor that provides an efficient area with low latency and high frequency. The DCT processor has sought to eliminate multiplication operations by increasing the amount of shift and addition operations using the CORDIC method to reduce the complexity of computation. Application such as Speech waveform 1D- DCT is a highly helpful application in signal processing. This research provides the power performance study and FPGA implementation of the CORDIC algorithm work for 111.048 MHz frequency 2D-DCT using HDL simulation with the help of Xilinx ISE 14.7 and implemented it using Development board Altera Cyclone ii.

关键词： image coding signal processing algorithms Computer architecture Very large scale integration Throughput Complexity theory Timing

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TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature signals

TCCT-Net: Two-Stream Network Architecture for Fast and Effic...

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IEEE Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Alexander Vedernikov Puneet Kumar Haoyu Chen Tapio Seppänen Xiaobai Li Center for Machine Vision and Signal Analysis University of Oulu Finland State Key Laboratory of Blockchain and Data Security Zhejiang University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need large amounts of input data, computational power, inference time. These constraints challenge embedding systems into devices for real-time use. To address these limitations, we present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain and boost processing speed, we introduce a "TC" stream that uses Continuous wavelet Transform (CWT) to represent information in a 2D tensor form. Evaluated on the EngageNet dataset, the proposed method outperforms existing baselines, utilizing only two behavioral features (head pose rotations) compared to the 98 used in baseline models. Furthermore, comparative analysis shows TCCT-Net’s architecture offers an order-of-magnitude improvement in inference speed compared to state-of-the-art image-based Recurrent Neural Network (RNN) methods. The code will be released at https://***/vedernikovphoto/TCCT_Net.

关键词： Convolutional codes Accuracy Recurrent neural networks Continuous wavelet transforms wavelet domain Convolution Computational modeling

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A Framework For Single image Dehazing Using DWT Based Cross Bilateral Filter Fusion of Generative and ASM Models

A Framework For Single Image Dehazing Using DWT Based Cross ...

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International conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Banala Revanth Sanjay K. Dwivedi Manoj Kumar Department of Computer Science Babasaheb Bhimrao Ambedkar University Lucknow India

ISBN: (纸本)9781665474177

Due to the latest advancements in technologies like artificial intelligence, machine learning, and deep learning in image processing, computer vision, and their uses in various applications, there has been a lot of interest in the restoration of murky images. This paper gives a novel single-image dehazing (SID) framework for the restoration of single hazy image (SHI). The framework has three parts. The first part of the framework comprises three networks. A white-balanced auto-encoder network in the front, followed by a pair of sequential auto-encoder networks at the end for image dehazing (ID). A white balance auto-encoder network is used for correcting the white balance error in the input hazy image (HI). Pair of sequential auto- encoder networks gives haze-reduced output by taking corrected white balanced error image as input. The second part comprises an ASM-based model Dark Channel Prior (DCP) for ID. DCP produce haze-free images (HFI) by taking HI as input. The third part presents the fusion model for integrating the first and second parts of the framework. DWT-based cross bilateral filter model is the fusion model (FM) to get the final HFI. The first part of the framework is trained by using a hybrid loss function (perceptual loss function combined with MSE loss function) on O- HAZE, I-HAZE, and MRFID data sets. The proposed framework got considerably good result in terms of Structural Similarity Index (SSIM), Peak signal-to-noise ratio (PSNR), compared with cutting-edge methods.

关键词： Training Measurement Learning systems PSNR Benchmark testing Discrete wavelet transforms image restoration

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FPGA vs DSP: A throughput and power efficiency comparison for Hierarchical Enumerative Coding

FPGA vs DSP: A throughput and power efficiency comparison fo...

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IFIP International conference on Very Large Scale Integration (VLSI-SoC)

作者： Yuhui Bai Syed-Zahid Ahmed Imen Mhedhbi Khalil Hachicha Cédric Champion Patrick Garda Bertrand Granado LIP6 CNRS Paris France ETIS CNRS Cergy France APC CEA Paris France

A comparative study of Hierarchical Enumerative Coding (HENUC) for FPGA and DSP implementation is presented. HENUC is a lossless fixed-point entropy coding algorithm employed by a wavelet-based image encoder, which provides good compression performance for the locally stationary image data. It has been implemented in our previous work on an Altera's 40nm Stratix IV EP4SGX230 FPGA as a hardware IP accelerator in a Nios ii based system. In this paper, we implemented it on a Texas Instruments's (TI) 40nm Integra C6A816x/AM389x DSP. We present experimental results regarding the execution time, resource utilization and core power consumption of the two implementations and we evaluate their throughput and power efficiency. Our results show that a highly parallelized FPGA implementation at 100MHz is 12.3× faster than a highly tuned DSP implementation running at 1.5 GHz and consumes 2.4× less power, they also show that the proposed algorithm is more suitable for hardware implementation.

关键词： image coding Digital signal processing Field programmable gate arrays Encoding Hardware Optimization Software

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HYPERSPECTRAL CODED APERTURE (HYCA): A NEW TECHNIQUE FOR HYPERSPECTRAL COMPRESSIVE SENSING

HYPERSPECTRAL CODED APERTURE (HYCA): A NEW TECHNIQUE FOR HYP...

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European signal processing conference

作者： Gabriel Martin Jose M. Bioucas-Dias Antonio Plaza Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura Escuela Politecnica de Caceres Instituto de Telecomunicacoes Instituto Superior Tecnico Torre Norte Piso 10 Av. Rovisco Pais

ISBN: (纸本)9781479936878

Hyperspectral imaging is an active research area in remote sensing. Due to the high volume of hyperspectral image data, the exploration of compression strategies has received a lot of attention in recent years. In this paper, we introduce a new compressed sensing methodology, termed Hyperspectral coded aperture (HYCA), which exploits the high correlation existing among the components of remotely sensed hyperspectral data sets to reduce the number of measurements necessary to correctly reconstruct the original data. HYCA relies on two central properties of most hyperspectral images: i) the spectral vectors live on a low dimensional subspace and ii) the spectral bands are piecewise smooth. The former property allows to represent the data vectors using a small number of coordinates, and the latter implies that each coordinate is piecewise smooth and thus compressible on local differences. The reconstruction of the data cube is obtained by minimizing a convex objective function containing a data term associated to the compressed measurements and a total variation spatial regularizer. A series of experiments with simulated and real data show the effectiveness of the newly developed HYCA, indicating that the proposed scheme has a high potential in real-world applications.

关键词： Hyperspectral imaging Compressive sensing Coded aperture signal subspace Total variation Optimization

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Heating Energy Performance Assessment using KNN Classification method: Investigating Test Size, Neighbor Parameter, and Class Count Effects

Heating Energy Performance Assessment using KNN Classificati...

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International conference on image and signal processing and their applications (ISPA)

作者： Aissa Boudjella Manal Yasmine Boudjella Chuang Liu Dept. of Applied Mathematics Samarkand International University of Ttechnology Samarkand Uzbekistan Dept. Electrical Engineering Bircham International University Miami USA Dept. of Physics University of Sciences and Technology USTO Oran Algerie Dept. of Thermal Engineering Samarkand International University of Technology Samarkand Uzbekistan

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

We analyze the energy requirements for assessing the heating load of building systems using the k-Nearest Neighbor (KNN) method for classification. Unsupervised transformations of the dataset are employed in learning algorithms. Two distinct data representation classes are generated separately from the original dataset based on captured heating load magnitudes. The unsupervised transformations of the dataset yield 4 and 8 classes, *** evaluate performance, various test and training sizes are employed, comparing measurement outcomes and predicting class label descriptions for two dataset representations as the parameter for the nearest neighbor (k) ranges from 1 to 20. Results obtained from the simulation indicate that accuracy is influenced by factors such as test size, the k parameter, and dataset class count. The training and test accuracy rates reach maximum values of 95% and 85%, respectively. Keeping k constant, the test size has a negligible impact on accuracy. The classes exhibit three critical regions: 1) Region I, where accuracy fluctuates with increasing k in the interval [1]-[5], and 2) Region ii, where accuracy declines with increasing k in the interval [5]-[10]. In Region iiI k [10]-[20], accuracy remains relatively constant. While reducing the number of classes may result in a slight improvement in accuracy by simplifying the classification task, the trade-offs and complexities of increasing the number of classes for data representation should be carefully considered. Although this approach can lead to a more flexible design, it may slightly reduce accuracy.

关键词： Heating systems Training Shape Buildings Machine learning signal processing Size measurement

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Cotton Jassid Infestation Monitoring Using Sentinel-2 MSI, Landsat 9 TIRS, and PlanetScope imagery with Machine Learning and Data Fusion

Cotton Jassid Infestation Monitoring Using Sentinel-2 MSI, L...

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International conference on Computer and applications (ICCA)

作者： Souleymane Bah M. D. Tanguy Mesreil Houegbe Emmanuel Wilfried G. Megnigbeto Vinasetan Ratheil Houndji Bignon Nicanor Kouton N. Gérard Gouwakinnou C. Eugène Ezin Institut de Mathématiques et de Sciences Physiques Université d'Abomey-Calavi Dangbo Bénin BiPLAC Laboratoire d'Ecologie de Botanique et de Biologie végétate Parakou Benin Division protection phytosanitaire Institut de Recherche sur le Coton Parakou Bénin Institut de Formation et de Recherche en Informatique Université d'Abomey-Calavi Abomey-Calavi Benin

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The excessive use of synthetic pesticides in the fields is the cause of numerous problems to human health and biodiversity. Knowing the location and severity of the emergence of pests enables targeted treatment, helping to reduce the use of pesticides. Given that traditional field inspection requires considerable effort with limited effectiveness, remote sensing combined with machine learning is an essential complement for large-scale, efficient monitoring. This study therefore aims to develop a machine learning-based solution for detecting cotton jassids from free satellite imagery data in Alibori, northern Benin. Multispectral and thermal infrared images respectively from the Sentinel-2 and Landsat 9 satellites were fused in the most correlated bands using signal processing algorithms Pseudo Wigner Distribution (PWD), Nonsubsampled Contourlet Transform (NSCT), and Discrete wavelet transform (DWT). Two configurations, in terms of data augmentation, were used. Areas infested by jassids were identified in merged and unmerged satellite images (Sentinel-2, Landsat 9, and PlanetScope, a commercial satellite) using Support Vector Machine (SVM), Random Forest (RF), and CatBoost supervised machine learning algorithms. The green and Thermal Infrared Sensor 1 (TIRS1) bands are the most correlated in configuration 1 and the blue and TIRSI bands in configuration 2 for respectively Sentinel-2 and Landsat 9. The DWT and NSCT signal processing algorithms produced the least Root Mean Square Deviation (RMSE) for image fusion (0.46 for DWT and 0.33 for NSCT). The best result in terms of detecting performance was obtained with the composite treatment of Landsat 9 images and the RF algorithm, with an overall accuracy of 88.23%, an Fl-score of 82%, and a Kappa index of 79.92% in configuration 1 and in configuration 2 for composite treatment of Sentinel-2 an overall accuracy of 92.36%, an Fl-score of 90.75 % and a Kappa index of 86.36% with CatBoost algorithm.

关键词： Radio frequency Support vector machines Satellites Machine learning algorithms signal processing algorithms Landsat Discrete wavelet transforms Satellite images Cotton Monitoring

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signal Restoration with Fractional Transform Sparse Representation and Autoencoder

Signal Restoration with Fractional Transform Sparse Represen...

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International conference on Computer and Communication Engineering, ICCCE

作者： Shivani Saxena Surbhi Mistry Nilesh Patel Mohendra Roy Ahsan Z. Rizvi Computer Sciences and Engineering Institute of Advanced Research Gandhinagar India Biotechnology and Bioengineering Institute of Advanced Research Gandhinagar India Information and Communication Technology Pandit Deendayal Energy University Gandhinagar India

Signa1 denoising is a crucial task in image processing, audio analysis, and communications. Traditional methods often struggle to strike a balance between reducing noise and preserving important signal details. Existing methods exploit the potential of fractional transforms combined with sparse representation techniques. However, these approaches face challenges in achieving the desired balance due to limited sensitivity to signal variations, high computational complexity, restricted adaptability to diverse noise types, and the need for careful selection of transform parameters. To overcome these limitations, we propose a novel method called Fractional Transform Sparse Representation Denoising (FTSRD), which integrates fractional wavelet transform (FWT), fractional DCT (FDCT), sparse representation, and Deep Denoising Autoencoder (DDAE) techniques. The integration of DDAE in FTSRD provides several advantages. DDAE, being a deep learning-based model, has the capability to learn complex noise patterns and extract meaningful signal features. Experimental evaluations were conducted on synthetic signals and ECG signals. The results demonstrate the superior performance of FTSRD in reducing noise while preserving essential structural details. It also surpasses traditional methods in subjective and objective quality measures. In conclusion, FTSRD addresses the challenges associated with noise reduction and signal detail preservation, making it a valuable tool in various denoising applications.

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