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Deep Learning Utilization in Beamforming Enhancement for Medical Ultrasound

Deep Learning Utilization in Beamforming Enhancement for Med...

引用

ieee Annual International Computer Software and Applications Conference (COMPSAC)

作者： Mariam Fouad Yousef Metwally Georg Schmitz Michael Huebner Mohamed A. Abd El Ghany Ruhr University Bochym (RUB) Germany and German University in Cairo (GUC) Egypt German University in Cairo (GUC) Egypt Ruhr University Bochum (RUB) Germany BTU Cottbus - Senftenberg Germany German University in Cairo (GUC) Egypt and TU Darmstadt (TUD) Germany

ISBN: (数字)9781728173030

ISBN: (纸本)9781728173047

Ultrasound imaging offers a low cost, noninvasive and portable system, which allowed it to be an invaluable tool for medical imaging. However, the quality of the reconstructed images depends significantly on the beamforming technique utilized. Although advanced data-adaptive methods of reconstruction such as Minimum Variance (MV) beamforming can recover image quality much higher than conventional techniques, their implementation also entails a heavy computational burden. This dichotomy hinders the ultrasound imaging use as a standalone device in some applications such as early breast cancer detection. Deep neural networks (DNNs) have shown a huge potential when applied to many Artificial intelligence (AI) research fields. In this work, the use of Deep learning in improving the quality of the beamforming technique Delay and Sum (DAS) normally used for ultrasound (US) images reconstruction is explored. Three different architectures are implemented: Convolutional AutoEncoder (CAE), Fully Connected network (FC) and U-Net-like architecture. They were trained on datasets simulated using field II. The dataset consists of input-output pairs where the input is Noisy DAS beamformed scan lines and the output is MV beamformed non-noisy scan lines. The networks show a great ability in predicting the beamformed signals along with significantly reducing noise in the reconstructed images. Additionally, the proposed networks improve other image characteristics such as scatterer size and position along with reducing tail characteristic normally found in DAS beamformed ultrasound images. US images constructed by the networks achieved better quality metrics that surpass conventional DAS beamformed images. The CAE, U-net-like architecture, and FC enhanced the signal to noise ratio (SNR) compared to DAS by 218%, 165% and 136% respectively. Additionally, the networks showed higher Contrast to Noise Ratio (CNR) and Contrast Ratio (CR) metrics than DAS beamformed signals. Finally, the propo

关键词： Array signal processing Ultrasonic imaging image reconstruction Delays Imaging Computer architecture Covariance matrices

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PC-BNA: Parallel Convolution Binary Neural Network Accelerator

PC-BNA: Parallel Convolution Binary Neural Network Accelerat...

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ieee International Conference on High Performance Computing and Communications (HPCC)

作者： Haihui Liu Yuang Zhang Xiangwei Zheng Mengxin Yu Baoxu An Xiaojie Liu Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology School of Information Science and Engineering Shandong Normal University Jinan China

Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dataset is obviously low, and the input layer uses 32-bit floating point to avoid excessive precision loss, which requires additional computing units and increases the computational burden. Therefore, it is very important to improve the input layer, save computing resources and reduce precision loss. In this paper, we propose a parallel convolution binary neural network accelerator architecture (PC-BNA). Based on our proposed BNN model, we design an efficient field programmable gate array (FPGA) based accelerator. The input of the first layer is binarized, the traditional binary convolution layer is replaced by a parallel binary convolution, and the network building blocks are improved. The experimental results show that the proposed PC-BNA has higher accuracy and better performance on CIFAR-10. The image recognition accuracy is as high as 91.4%, which is superior to the state-of-art of the BNN accelerator. Compared to the state-of-the-art BNN model, when using the same model size, the use of look-up-table (LUT) saves 9.08%, and the digital signal processor (DSP) saves 27.7%. The results suggest that it is promising for PC-BNA in future high performance mobile artificial intelligence applications.

关键词： Performance evaluation Convolution Neural networks Digital signal processors Speech recognition Logic gates Mobile handsets

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Advanced Speech processing for Speaker Authentication in Communication Systems

Advanced Speech Processing for Speaker Authentication in Com...

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International symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Vinod Gujral Jetendra Joshi Praveen Medikonda Nishchay Grover Student Dept. of ECE NIIT University Rajasthan India Assistant Professor Dept. of ECE NIIT University Rajasthan India Student Dept. of CSE NIIT University Rajasthan India

ISBN: (纸本)9781538681350;9781538681343

In this era of artificial intelligence and machine learning, there are many small as well as large-scale companies and organizations, which are working on projects to produce goods and services, these are intelligent enough to perform task on their own. The major focus in building intelligent machines leads to the replication of senses of hearing and sight, which involves the use of audio and image processing. Through this paper, we are proposing a technique devised by us during our research and development project based on research we did on existing techniques in terms of their performance and how we can use them in a combination. The technique, which we have proposed in this paper, is for recognizing and authenticating the speaker based on both frequency and phase features of the speech and analyzing it on frame-by-frame basis. It has often to see the problem of corrupted/synthetic speech, which allows unrestricted access in different situations or in simple terms, we can say that anyone can spoof the system. This happens due to limited dataset or a dataset, which contains very less samples of synthetic speech. Due to this, it becomes difficult to train the model and we get negative results. Therefore, in this research we have devised a technique and tested it to eliminate such problems. Further, we have utilized this technique in the implementation of sending voice messages in a Vehicular Ad-Hoc Network (VANETs) and because of this technique its easier for a driver to trust on the message sent by an another driver in the network.

关键词： Feature extraction Speech recognition Data models Training Speech synthesis Delays computational modeling

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Enhancing Stock Market Predictions with Granger and Reinforcement Learning: An Adaptive Approach

Enhancing Stock Market Predictions with Granger and Reinforc...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Chiayu Hsu Lin-Sheng Lee Jenhui Chen Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Traditional graph neural networks (GNNs) construct static graph structures, which are unable to dynamically adapt to market changes. To address this challenge, this paper proposes an integrated prediction model combining GNNs and reinforcement learning (RL), enabling dynamic adjustment of graph structures to enhance the model's adaptability and predictive ability in volatile markets. Firstly, we use GNNs to extract complex inter-stock relationships from historical stock data, constructing a dynamic market structure. Secondly, through RL strategies within the RL model, we continuously adjust trading decisions to maximize long-term returns and adapt to market changes. This combined model not only captures market trading information but also responds promptly to market dynamics, achieving more accurate predictions. Experimental results on multiple datasets of Taiwan 50 constituent stocks demonstrate that our proposed GNN-RL model outperforms traditional methods in terms of accuracy and other metrics, proving its potential and superiority in practical applications.

关键词： Measurement Adaptation models Accuracy computational modeling Decision making Reinforcement learning Predictive models Data models Stock markets Forecasting

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Research on Wind Turbine Power Prediction Based on Model Fusion

Research on Wind Turbine Power Prediction Based on Model Fus...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Xiuxia Zhang Jian Hao Shuyi Wei North Minzu University School of Information Engineering Yinchuan China Xi’an Jiaotong University School of Mechanical Engineering Xi’an China

ISBN: (纸本)9781665426565

Wind power output is influenced by many factors, and its changing trend is complex, so it is difficult to use a single forecasting method to effectively forecast wind power. Moreover, an excellent power prediction model will have a significant impact on rational energy dispatching, demand management, energy conservation and emission reduction. In this paper, based on the classic machine learning model, multiple models are fused to predict the power and optimize the performance of energy. A number of algorithm models are used as base learners to fuse the weights of the prediction results to keep the feature relevance. Then, the weight fusion models are also used as base learners for training, and further high-level models are obtained through Stacking model fusion, which is compared with a number of classical algorithm models to synthesize the best performance energy prediction model. By comparison, the power prediction model fused by multiple models has higher running speed and accuracy, and higher performance in energy power prediction. The results show that the multiple model fusion of classical algorithm models can effectively improve the accuracy of power prediction, thus obtaining the best performance power prediction model.

关键词： Training Machine learning algorithms computational modeling Stacking Predictive models Wind power generation Prediction algorithms

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Optimizing Seed Set for New User Cold Start

Optimizing Seed Set for New User Cold Start

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ieee symposium Series on computational intelligence

作者： He-Da Wang Ji Wu Multimedia Signal and Intelligent Information Processing Laboratory Department of Electronic Engineering Tsinghua University

ISBN: (纸本)9781479975617

Users newly enter a recommender system can not get personalized recommendation due to the lack of personal profiles. An interview process that asks new users to rate a set of items (the seed set) will help user modeling and improve user experience. Traditional seed set generation approaches often concentrate on item-wise properties instead of aiming at finding the optimal seed set. We propose a simple random optimization technique to search for the optimal seed set, which considers the seed set as a whole and performs a random search by reducing the prediction error on validation set. By off-line experiments on the MovieLens 10M data set, we show that the proposed approach performs as well as the state-of-the-art method called GreedyExtend, and the proposed approach needs significantly less computational cost to reach the same prediction error as the best baseline on validation set.

关键词： Seed set Error of prediction Recommender systems user modelling Cold starting TEST SETS Consumers

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DDoS Attack Detection Based on Random Forest

DDoS Attack Detection Based on Random Forest

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Yini Chen Jun Hou Qianmu Li Huaqiu Long Nanjing University of Science and Technology Nanjing China Nanjing Vocational University of Industry Technology Nanjing China Wuyi University Jiangmen China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

With the development of network technology, distributed denial of service (DDoS) attacks have increasingly become an important security risk that endangers the network. It uses common protocols and services when attacking, so it is difficult to detect through traditional methods. Based on the idea of rational thinking, DDoS attack detection can be simulated as a classification problem that distinguishes between "rational" and "irrational" network flow states. This article analyzes the common TCP flood attacks, UDP flood attacks, and ICMP flood attacks in detail. Define the characteristics of data stream information entropy (DSIE) to characterize attack behavior. A DDoS attack detection method based on random forest classification (RFC) model is proposed. Establish classification models for the above three types of typical attack methods. Through training and learning, it is finally predicted whether the network traffic is normal. Experimental results show that the RFC model can more accurately distinguish between normal traffic and attack traffic, with a higher detection rate and a lower false alarm rate.

关键词： computational modeling Hidden Markov models Denial-of-service attack Floods Computer crime Information entropy Random forests

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An End-to-End Basis-Spline Based Text Spotting Network for Scene Text

An End-to-End Basis-Spline Based Text Spotting Network for S...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Ningli Xu Zhichao Lian Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

Scene text spotting is considered as an essential step for many applications in text-based visual question answering (VQA), such as auto scoring system, image caption retrieval and keyword spotting (KWS). The performance of a text-based VQA system is largely based on its text detection accuracy. Since irregular text accounts for large proportion in outdoor text-based VQA system, traditional text spotting algorithms for handling regular text has been disappointing. The recent text detection algorithms are mainly divided into two categories. They are regression-based methods and segmentation-based methods. Regression-based methods cannot deal with perfectly with the text of various layout while segmentation-based methods require much computation cost to post-process the pixel-wise prediction result. Thus, we propose a basis-spline based text spotting model for robust irregular text spotting. We propose a basis-spline text representation method with few parameters to represent irregular text layout. With this novel basis spline curve representation, our model can precisely fit the text layout of various types. Finally, we propose a new text-based VQA dataset including images collected from a real-world computer operator qualification exam. Based on this dataset, we implement an auto scoring system and other state-of-the-art recognition algorithms that automatically provides a score corresponding to a student's feedback given a set of standard answers.

关键词： Visualization Shape computational modeling Layout Splines (mathematics) Standards Qualifications

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TransCL: Contrastive Learning on Complex Transportation Network

TransCL: Contrastive Learning on Complex Transportation Netw...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Rui Xue Guohua Li Xiaoning Ma Yifei Liu Min Liu Yanjun Liu China Academy of Railway Sciences Co. Ltd. Beijing China China Academy of Railway Sciences Beijing China

ISBN: (纸本)9781665426565

Networks in real life have been increasingly dependent on each other, and therefore, they have become more complex and intertwined, with consequences of relations that are difficult to identify, understand and represent. Besides, the coupling interactions among layers may vary in different types of complex networks. Thus, it is demanding to focus on this interdependence when the cost of taking inter-layer steps weights more in networks such as transportation. To obtain representative node embeddings in complex networks, we propose a solution collecting coupling relations among layers with contrastive learning. Specifically, we develop a framework, termed TransCL, with encoders in two aspects to embed intra-layer and inter-layer node representations. Besides, we introduce random walk betweenness centrality to the inter-layer embeddings and leverage this measurement to improve contrastive learning. The link prediction as a downstream task is followed to evaluate the embedding performance. We compare this method with other popular embedding models on the public dataset Cora and a real-world industrial dataset. This model outperforms other methods on the industrial dataset and meanwhile shows competitive performance on the public dataset. This work, in sum, allows for obtaining complex network representations with layer interdependence learned in a self-supervised manner.

关键词： Couplings Costs Conferences computational modeling Transportation Complex networks Predictive models

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Evaluating the Usability of a Social Storefront Business Model for Ecuadorian Millennials and Centennials

Evaluating the Usability of a Social Storefront Business Mod...

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Félix Fernández-Peña Fernando Ibarra-Torres Pilar Urrutia-Urrutia Diana Coello-Fiallos Research Group of Applied Informatics Universidad Técnica de Ambato Ambato Ecuador ESAI Business School Universidad Espíritu Santo Guayaquil Ecuador Career of Civil Engineering Universidad Técnica de Ambato Ambato Ecuador

ISBN: (纸本)9781665426565

Social media has been influencing almost all areas of human society during the last decade. Nevertheless, the lack of studies on the acceptance of a social storefront business model has been identified as a missing point in this research area. In this scenario, this paper evaluates the technology acceptance of a storefront website and its Facebook business page. The study was carried out with the participation of 520 young students of two Ecuadorian universities from the Coastal and the Andean region. A controlled experiment was designed based on the Technology Acceptance Model. After using the evaluated tools for two weeks, two surveys were conducted and 48 participants were interviewed. The results show that both, the perceived ease of use and the perceived usefulness of the analyzed tools have a positive impact on the behavioral intention of using social media for e-commerce. No significant differences were found but both, the storefront webpage and the Facebook business page, had positive acceptance among participants. Interviewed students sustained that social media play a key role for reinforcing the trustability of e-commerce initiatives in the country.

关键词： Technology acceptance model Social networking (online) Conferences computational modeling Sociology Sea measurements Electronic commerce

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