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Voice Command Recognition for Drone Control by Deep Neural Networks on Embedded System 8

Voice Command Recognition for Drone Control by Deep Neural N...

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8th international conference on Electrical and Electronics Engineering (ICEEE)

作者： Yapicioglu, Cengizhan Dokur, Zumray Olmez, Tamer Istanbul Tech Univ Dept Elect & Commun Engn Istanbul Turkey

ISBN: (纸本)9781665440714

Speech recognition and its applications for controlling a system has been an important and attractive issue over the last few decades. Controlling electronic devices by speech commands allows us to manage systems quickly and easily since users would not need any additional information or remote controller. Being able to communicate a system by using speech commands also brings with the requirements of fast and accurate response. So, at the present, speech recognition algorithms are extensively performing on high performance computers. However, the improvements of system on a chip (SoC) board and deep neural network based algorithms, make it possible to execute such kind of programs on them. The proposed study defines a model for controlling a drone system by using Turkish speech directional commands in real time which is based on deep learning approaches using spectrogram images. At first, speech commands are detected in real time with the help of signal energy and zero crossing rate and these are converted to log spectrogram images. A CNN (three convolutional layers and a fully connected layer) structure is created and trained by feeding those images. Then, the trained model is moved to embedded board to achieve real time, low-cost performance. Speech commands are provided by the user instantaneously and transferred to the model as an input for decision. Then, algorithm decides which directional command is given by the user and desired operation is performed on the drone system. It is observed that, by using the proposed model, accuracies of 95.72% for offline dataset and 92,88% for real time classification are obtained.

关键词： speech recognition spectrogram convolutional neural networks deep learning speech processing image processing embedded systems

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An Enhanced Bats Algorithm for Optimization Thresholds in Medical image Segmentation 17th

An Enhanced Bats Algorithm for Optimization Thresholds in Me...

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17th international conference on Intelligent Information Hiding and Multimedia signal processing (IIH-MSP) / 14th international conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Dao, Thi-Kien Vu, Ngoc-Thanh Nguyen, Thi-Xuan-Huong Nguyen, Trong-The Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fujian Peoples R China Hai Phong Univ Med & Pharm Haiphong Vietnam Haiphong Univ Management & Technol Haiphong Vietnam VNU HCM Univ Informat Technol Multimedia Communicat Lab Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City 700000 Vietnam

ISBN: (纸本)9789811910531;9789811910524

The key to threshold segmentation is choosing the thresholds that will decide the segmentation's outcome-the calculation complexity increases as the number of thresholds increases, which causes challenges with traditional methods. This study introduces an enhanced bats algorithm (EBA) for selecting optimal picture segmentation thresholds and applies it to the global optimization issue of the segmentation function's objective function. The EBA blends well with picture segmentation that produces exceptional computation in global convergence and robustness and prevents trapping into local optimization. It is particularly well suited to solving complex functions with high dimensions and multiple peaks. Compared to GA and PSO, extensive theoretical study and simulation results reveal that EBA has greater efficacy, efficiency, stability of the range of thresholds, and quality in multi-image and multi-thresholds segmentation.

关键词： Enhanced bats algorithm Kapur entropy Optimization algorithms Picture threshold segmentation

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A Deep Optimization image Deblurring Model Based on Internal and External Information 9

A Deep Optimization Image Deblurring Model Based on Internal...

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9th international conference on Communication, image and signal processing, CCISP 2024

作者： Tan, Yongqun Zhang, Lingli School of Mathematics and Statistics Chongqing Jiaotong University Chongqing400074 China Chongqing University of Arts and Sciences Chongqing Key Laboratory of Statistical Optimization and Complex Data Chongqing402160 China Chongqing University of Arts and Sciences Chongqing Key Laboratory of Group & Graph Theories and Applications Chongqing402160 China

ISBN: (纸本)9798350356656

In recent decades, there has been a notable focus on addressing image deblurring problem. Different deblurring algorithms integrate prior information into the image deblurring model, and the effectiveness of the prior information directly determines the performance of the deblurring. This paper presents a deep optimization image deblurring model based on internal and external information, which is joint with total variation, non-local self-similarity and convolutional neural network. The corresponding algorithm of the presented model utilizes half quadratic splitting method to decouple the fidelity and regularization terms, which can be solved through the sub-problems, respectively. Numerical experiments show that the presented algorithm can outperform the existing algorithms both in preserving the edges and assessment indicators (PSNR, RMSE, and SSIM). © 2024 IEEE.

关键词： Optimization algorithms

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Biologically Inspired Techniques in Many Criteria Decision Making 1

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丛书名： Smart Innovation, Systems and Technologies

1000年

作者： Satchidananda Dehuri Bhabani Shankar Prasad Mishra Pradeep Kumar Mallick Sung-Bae Cho

ISBN: (数字)9789811687396

ISBN: (纸本)9789811687389;9789811687419

This book includes best-selected, high-quality research papers presented at Second international conference on Biologically Inspired Techniques in Many Criteria Decision Making (BITMDM 2021) organized by Department of Information & Communication Technology, Fakir Mohan University, Balasore, Odisha, India, during December 20-21, 2021. This proceeding presents the recent advances in techniques which are biologically inspired and their usage in the field of many criteria decision making. The topics covered are biologically inspired algorithms, nature-inspired algorithms, multi-criteria optimization, multi-criteria decision making, data mining, big-data analysis, cloud computing, IOT, machine learning and soft computing, smart technologies, crypt-analysis, cognitive informatics, computational intelligence, artificial intelligence and machine learning, data management exploration and mining, computational intelligence, and signal and image processing.

关键词： Computational Intelligence Artificial Intelligence algorithms signal, image and Speech processing

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Channel Estimation for Underwater Acoustic OFDM Communications: An image Super-Resolution Approach

Channel Estimation for Underwater Acoustic OFDM Communicatio...

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IEEE international conference on Communications (ICC)

作者： Ouyang, Donghong Li, Yuzhou Wang, Zhizhan Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

ISBN: (纸本)9781728171227

In this paper, by exploiting the powerful ability of deep learning, we devote to designing a well-performing and pilot-saving neural network for the channel estimation in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communications. By considering the channel estimation problem as a matrix completion problem, we interestingly find it mathematically equivalent to the image super-resolution problem arising in the field of image processing. Hence, we attempt to make use of the very deep super-resolution neural network (VDSR), one of the most typical neural networks to solve the image super-resolution problem, to handle our problem. However, there still exist significant differences between these two problems, we thus elegantly modify the basic framework of the VDSR to design our channel estimation neural network, referred to as the channel super-resolution neural network (CSRNet). Moreover, instead of training an individual network for each considered signal-to-noise ratio (SNR), we obtain an unified network that works well for all SNRs with the help of transfer learning, thus substantially increasing the practicality of the CSRNet. Simulation results validate the superiority of the CSRNet against the existing least square (LS) and deep neural network (DNN) based algorithms in terms of the mean square error (MSE) and the bit error rate (BER). Specifically, compared with the LS algorithm, the CSRNet can reduce the BER by 44.74% even using 50% fewer pilots.

关键词： Deep learning OFDM Simulation Superresolution Neural networks Bit error rate Transfer learning

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Moment Transform-Based Compressive Sensing in image processing 28th

Moment Transform-Based Compressive Sensing in Image Processi...

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28th international conference on Systems, signals and image processing (IWSSIP)

作者： Kalampokas, Theofanis Papakostas, George A. Int Hellen Univ Dept Comp Sci MLV Res Grp Kavala Greece

ISBN: (纸本)9783030968786;9783030968779

Over the last decades, images have become an important source of information in many domains, thus their high quality has become necessary to acquire better information. One of the important issues that arise is image denoising, which means recovering a signal from inaccurately and/or partially measured samples. This interpretation is highly correlated to the compressive sensing theory, which is a revolutionary technology and implies that if a signal is sparse then the original signal can be obtained from a few measured values, which are much less, than the ones suggested by other used theories like Shannon's sampling theories. A strong factor in Compressive Sensing (CS) theory to achieve the sparsest solution and the noise removal from the corrupted image is the selection of the basis dictionary. In this paper, Discrete Cosine Transform (DCT) and moment transform (Tchebichef, Krawtchouk) are compared in order to achieve image denoising of Gaussian additive white noise based on compressive sensing and sparse approximation theory. The experimental results revealed that the basis dictionaries constructed by the moment transform perform competitively to the traditional DCT. The latter transform shows a higher PSNR of 30.82 dB and the same 0.91 SSIM value as the Tchebichef transform. Moreover, from the sparsity point of view, Krawtchouk moments provide approximately 20-30% more sparse results than DCT.

关键词： Compressive sensing image moments Denoising Tchebichef moments Krawtchouk moments DCT

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Single Color image Dehazing Based on Variational Model 6

Single Color Image Dehazing Based on Variational Model

引用

6th IEEE international conference on Intelligent Computing and signal processing, ICSP 2021

作者： Zhao, Shengnan Yang, Hao Zhao, Yonghao Yancheng Teachers University School of Information Engineering Yancheng China

ISBN: (纸本)9780738143705

Combining variational approach with the atmospheric attenuation model, two single color image dehazing algorithms on the basis of dark channel prior and MTV model are proposed which are called H-MTV model and NL-H-MTV model. Then, we use Dual Bregman algorithm to calculate the H-MTV model and Split Bregman algorithm to calculate the NL-H-MTV model. We conducted three experiments to adjust the parameters. Finally, comparing the proposed models with He algorithm and Kimmel Retinex algorithm. Experimental results show that the proposed methods can remove fog while keeping texture features effectively. © 2021 IEEE.

关键词： Color

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Edge Maps Assisted image Super-Resolution using Spatial Feature Transform 12

Edge Maps Assisted Image Super-Resolution using Spatial Feat...

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12th international conference on Graphics and image processing (ICGIP)

作者： Waseem, Ahmad Liu, Ye Wan, Liang Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

ISBN: (纸本)9781510642782;9781510642775

Deep learning has revolutionized every field of computer vision, including single image super-resolution because of its remarkable performance pertaining to effectiveness and efficiency. Various recent methods try to predict the SR image by incorporating different prior knowledge of images. In this paper, we propose a new method that utilizes not only internal image features but also multi-level edge prior knowledge with richer information. Holding the intuition that edge information helps to deal with blurry edges and try to generate sharper results, we present a residual edge and channel attention super-resolution network to handle LR images, named RECAN. Our architecture consists of two basic modules: the first module is EdgeNet, which generates multi-level edge maps from the input image;and the second module takes advantage of significant information in input image along with edge maps, called SRNet. Specifically, the SRNet uses channel attention technique and spatial feature transform (SFT) layers to super-resolve an image. Qualitative and quantitative comparisons are presented with state-of-the-art methods, which show promising results of our method together with improved image quality.

关键词： Single image Super-Resolution Edge Information Channel Attention Spatial Feature Transform

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Research of intertidal remote sensing image fusion method based on logical block 7th

Research of intertidal remote sensing image fusion method ba...

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7th international conference on signal and Information processing, Networking and Computers, ICSINC 2020

作者： Xing, Jian Wu, Lina Dong, Shilin Institute of Spacecraft System Engineering CAST Beijing100094 China Institute of Spacecraft Application System Engineering CAST Beijing100094 China

ISBN: (纸本)9789813341012

The intertidal zone refers to the coast between the average highest and lowest tidal levels, which is an important area for researching the dynamics of modern sea-land environments and utilizing the marine resources. The intertidal zone is affected by the dual forces of sea and land, and also human activities. The existing land area remote sensing image processing methods are not completely suitable for intertidal zones, and the accuracy and efficiency of image fusion processing methods need to be improved. A method of the fusion of large scale data remote sensing images for the land and sea intertidal zone is proposed. The logical block is used to divide the image and an image pyramid is established. Through the improvement of different fusion algorithms, the fusion of large scale data remote sensing images is achieved with a high accuracy and efficiency. The experiment was developed to prove that the IHS-NMF algorithm based on the logical block is effective in optimizing large scale remote sensing images. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image fusion

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Systems, signals and image processing 1

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丛书名： Communications in Computer and Information Science

1000年

作者： Gregor Rozinaj Radoslav Vargic

ISBN: (数字)9783030968786

ISBN: (纸本)9783030968779

This volume constitutes selected papers presented at the 28th international conference on Systems, signals and image processing, IWSSIP 2021, held in Bratislava, Slovakia, in June 2021. Due to the COVID-19 pandemic the conference was held online.;The presented 14 full and 5 short papers were thorougly reviewed and selected from the 76 submissions. The papers focus on various aspects of advanced signal processing in different scientific areas, including filter design, Fourier and other transforms, feature extraction, machine learning and system adaptation to user-oriented products like 5G networks, IoT, virtual teleport or tele-surgery operations.

关键词： Computer Imaging, Vision, Pattern Recognition and Graphics Information Systems and Communication Service Computer System Implementation Computer Communication Networks Machine Learning

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