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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

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Robust Spatiotemporal Fusion of Satellite images via Convex Optimization

Robust Spatiotemporal Fusion of Satellite Images via Convex ...

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international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Ryosuke Isono Kazuki Naganuma Shunsuke Ono Tokyo Institute of Technology

Spatiotemporal fusion (ST fusion) is a feasible solution to resolve a tradeoff between the temporal and spatial resolutions of satellite images. Although many ST fusion methods have been proposed, most methods have not been developed that explicitly take noise in observed images into account, despite the inevitable influence of noise caused by the observation equipment and environment. In this paper, we propose an optimization-based ST fusion method that is robust to noise. First, we introduce observation models for noisy satellite images and make certain assumptions on the relationship between the observed images and the target high-resolution image. Next, based on these models and assumptions, we formulate the fusion problem as a constrained optimization problem and develop an efficient algorithm based on a primal-dual splitting method for solving the problem. The performance of the proposed method was verified using simulated and real data, and the results illustrate that our method outperforms state-of-the-art ST fusion methods for both noiseless and noisy satellite images.

关键词： Satellites signal processing algorithms signal processing Convex functions Spatiotemporal phenomena Sensors Noise measurement

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Strain Measurement Method of Optical Fiber Composite Overhead Line based on Least-squares Fit

Strain Measurement Method of Optical Fiber Composite Overhea...

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2022 international conference on Manufacturing, Industrial Automation and Electronics, ICMIAE 2022

作者： Zhong, Chaoyi Wen, Bo Zhou, Wen Tan, Cuirong Huang, Zhuhui Zhao, Lijuan Guangdong Power Grid Corporation Heyuan Power Supply Bureau Heyuan China North China Electric Power University Hebei Key Laboratory of Power Internet of Things Technology Baoding China

ISBN: (数字)9781665473965

ISBN: (纸本)9781665473965

To improve the accuracy of measured strain for optical fiber composite overhead line, a strain measurement method based on the pseudo-Voigt profile and least-squares fit is proposed in this paper. Firstly, the principle of Brillouin scattering-based distributed optical fiber strain measurement method is presented. Secondly, the improved quadratic polynomial fit is used to calculate the initial value of variables. Thirdly, the objective function and optimization process of the least-squares fit are presented. Finally, the Brillouin spectra of the optical fiber composite overhead line with different values of SNR (signal-to-noise ratio) are numerically generated, and the corresponding Brillouin frequency shift (BFS) and strain are calculated by using the proposed method. The results show that the pseudo-Voigt profile-based least-squares fit can effectively approximate Brillouin spectrum under different values of frequency sweep span deviation and SNR, and the accuracy in BFS and strain is guaranteed. This work provides a high precision method for strain measurement of optical fiber composite overhead line. © 2022 ieee.

关键词： Overhead lines

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A Classification Algorithm of Grain Crop image based on Improved SqueezeNet Model 3

A Classification Algorithm of Grain Crop Image Based on Impr...

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3rd ieee international conference on Frontiers technology of Information and Computer, ICFTIC 2021

作者： Liu, Yang Zhao, Zhiheng Zhu, Jiangbo Shen, Zhichao Sun, Lei Harbin Institute of Technology School of Electrical Engineering and Automation Harbin China Shanghai Anxi Machinery Manufacturing Co. Ltd. Shanghai China

ISBN: (纸本)9781665406055

Aiming at the disadvantages of traditional crop screening methods, such as dependence on experience and low accuracy, a granular crop classification algorithm based on deep learning was proposed. This paper took peanuts as an example to construct an image data set. According to the damage of the peanut skin, it could be divided into two types: intact peanuts and peanuts with damaged skin. Then based on the Fire module in SqueezeNet, the convolutional neural network was improved. Using comprehensive optimization training strategies such as Msra initialization, RMSprop method and momentum method, the classification accuracy rate could reach 94.4%. Compared with the existing crop screening methods, the accuracy rate was significantly improved, and it was helpful to realize the screening and identification of granular crops. © 2021 ieee.

关键词： Crops

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A Double Fittings based Total Variation Model for image Denoising 6

A Double Fittings Based Total Variation Model for Image Deno...

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6th international conference on signal and image Processing, ICSIP 2021

作者： Li, Mengmeng Li, Bingzhao Beijing Institute of Technology School of Mathematics and Statistics Beijing China

ISBN: (纸本)9780738133737

In this paper, weighted image gradients based fitting is introduced into the proposed model for image denoising, which is called double fittings based total variation (DFTV) model. The weighted gradients are designed to maintain image structures during the denoising process. The definition of the weight function makes it better to enhance edges of images and maintain structures. For solving the proposed model, alternating direction method of multipliers (ADMM) is explored. In order to show the effectiveness and efficiency of the proposed model, both of the visual results and quantity evaluation are provided for compared experiments. © 2021 ieee.

关键词： image denoising

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Exploring the Landscape of Acoustic Authentication: Techniques, Performance and Security

Exploring the Landscape of Acoustic Authentication: Techniqu...

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international conference on technology, Informatics, Management, Engineering, and Environment (TIME-E)

作者： Andi Bahtiar Semma Kusrini Arief Setyanto Bruno Da Silva An Braeken Doctoral of Informatics Engineering Universitas AMIKOM Yogyakarta Yogyakarta Indonesia Magister of Informatics Enginering Universitas AMIKOM Yogyakarta Yogyakarta Indonesia Department of Electronics and Informatics Vrije Universiteit Brussel Brussels Belgium Department of Engineering Technology Vrije Universiteit Brussel Brussels Belgium

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

The internet of Things (IoT) is a rapidly growing network of interconnected devices, transforming our relationship with technology. The potential of the interconnected ecosystem relies on seamless device pairing. Previously, complex pairing processes hindered adoption due to user frustration. Bluetooth pairing, a standardized method for IoT device interaction, has limitations such as manual entry, error-proneness, and user intervention. This can hinder user experience and integration in multi-device ecosystems. Acoustic pairing is a potential alternative to traditional wireless communication, using sound waves as a communication channel between devices. Unlike conventional Bluetooth or Wifi pairing, it requires minimal user interaction and no additional hardware. The study result indicates that in acoustic authentication, On-Off Keying, FSK, GMSK, OFDM, and DFSK are the most common modulation techniques, while the effective range is from proximity (several mm) to maximum limited to acoustic signal propagation. Combining different modulation techniques could be a promising approach to address both high range and high success rates for secure acoustic communication. First, GMSK modulation achieved a Bit Mismatch Metric (BMM) of 0.5%-3% over 50-70cm. Second, a novel deep learning-based modulation that embeds information onto channel-specific features. This approach achieved a Bit Error Rate (BER) of less than 1% over a 25 cm 2 area. Lastly, OFDM achieved a success rate greater than 95% over 20 - 220cm. Those results indicate that combining different modulation techniques, such as GMSK, deep learning-based modulation, and OFDM, can be a promising approach to achieving both high range and high success rates for secure acoustic communication systems. In addition, most of the reviewed studies are resilient against common attacks.

关键词： Bluetooth OFDM Ecosystems Bit error rate Modulation Authentication Frequency shift keying Acoustics internet of Things Security

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Low-Complexity RR Interval-based Cardiac Dysrhythmia Indicator for Wearable ECG Devices

Low-Complexity RR Interval-Based Cardiac Dysrhythmia Indicat...

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ieee international conference on systems, Man and Cybernetics

作者： Yellappa Palagani Venkateswarlu Gonuguntla Postdoctoral Fellow in Cardiothoracic Surgery at Stanford University Symbiosis Centre for Medical Image Analysis Symbiosis International (Deemed University) Pune India

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Cardiac dysrhythmias pose a significant risk of sudden cardiac death, necessitating prompt detection and treatment. While wearable ECG devices offer real-time monitoring capabilities, current methods for identifying dysrhythmias are often time-consuming. In this paper, we present a novel low-complexity RR interval (RRI)-based Cardiac Dysrhythmia Indicator (CDI) aimed at efficiently detecting dysrhythmias. The CDI architecture comprises two principal components: the Slow-Fast-Normal Indicator Circuit (SFNIC) and the Normal-Abnormal Indicator Circuit (NAIC). SFNIC monitors dysrhythmias at each RRI, while NAIC evaluates dysrhythmia status at regular intervals, indicating normal or abnormal states every 64 RRIs (64 bpm is near to normal pulse rate). Through extensive simulations conducted using 0.18 μ $m$ CMOS processes, with mixed-signal circuits operating within the (0.9-1.8) volt range, our design demonstrates successful recognition of various ECG rhythms. These findings suggest that the proposed CDI design holds promise for integration into wearable ECG devices, enabling real-time dysrhythmia detection and timely intervention.

关键词： Semiconductor device modeling Rail to rail inputs Heart beat Electrocardiography Death Real-time systems Object recognition Biomedical monitoring Integrated circuit modeling Monitoring

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Cooperative Spectrum Sensing with Machine Learning Approaches in Cognitive Radio Networks for IoMT Applications

Cooperative Spectrum Sensing with Machine Learning Approache...

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ieee international conference on Industrial technology (ICIT)

作者： Haitham Mahmoud Tobi Baiyekusi Umar Daraz De Mi Ziming He Chen Lu Mingxiang Guan College of Computing Birmingham City University Birmingham United Kingdom Samsung Cambridge Solution Centre Cambridge United Kingdom Shenzhen Institute of Information Technology Shenzhen China

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

The increasing demand for internet of Medical Things (loMT) and connected healthcare adds to the cellular systems' limited bandwidth, leading to a scarcity of available fre-quencies. Spectrum sensing in cognitive radio networks (CRNs) can address this challenge by monitoring and accessing under-utilized frequencies, enhancing spectrum efficiency. Compared to several initiatives embracing CRN in generic loT, our work pioneeringly investigates on how CRNs uphold connectivity for the connected healthcare applications. The focus of this paper is on exploiting the robustness of machine learning (ML) algorithms in CRN and employing ML in identifying idle channel states in spectrum sensing for the 10MT application scenarios. Suitable ML algorithms, including SVM, KNN, Decision Tree, Random Forest, and Naive Bayes, are trained to detect primary users and locate vacant bands for optimal allocation, in the presence of secondary users whose data is generated by considering various factors, e.g., received signal strength, path loss, and different fading effects. The system performance is assessed in terms of accuracy, precision, recall, and score for all ML approaches. The numerical results demonstrate a considerable improvement in cooperative spectrum sensing accuracy, with a remarkable 20% reduction in false positives.

关键词： Support vector machines Cellular networks Radio frequency Machine learning algorithms Cooperative communication System performance internet of Medical Things

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Joint Beamforming for Backscatter Integrated Sensing and Communication

Joint Beamforming for Backscatter Integrated Sensing and Com...

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ieee conference on Global Communications (GLOBECOM)

作者： Zongyao Zhao Tiankuo Wei Zhenyu Liu Xinke Tang Xiao-Ping Zhang Yuhan Dong Shenzhen International Graduate School Tsinghua University Shenzhen P. R. China Pengcheng Laboratory Shenzhen P. R. China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Integrated sensing and communication (ISAC) is a key technology of next generation wireless communication. Backscatter communication (BackCom) plays an important role for internet of things (IoT). Then the integration of ISAC with BackCom technology enables low-power data transmission while enhancing the system sensing ability, which is expected to provide a potentially revolutionary solution for IoT applications. In this paper, we propose a novel backscatter-ISAC (B-ISAC) system and focus on the joint beamforming design for the system. We formulate the communication and sensing model of the B-ISAC system and derive the metrics of communication and sensing performance respectively, i.e., communication rate and detection probability. We propose a joint beamforming scheme aiming to optimize the communication rate under sensing constraint and power budget. A successive convex approximation (SCA) based algorithm and an iterative algorithm are developed for solving the complicated non-convex optimization problem. Numerical results validate the effectiveness of the proposed scheme and associated algorithms. The proposed B-ISAC system has broad application prospect in IoT scenarios.

关键词： Wireless communication Array signal processing Simulation Integrated sensing and communication Approximation algorithms internet of Things Optimization Next generation networking Backscatter signal to noise ratio

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image to image Translation Networks using Perceptual Adversarial Loss Function 7

Image to Image Translation Networks using Perceptual Adversa...

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7th ieee international conference on signal and image Processing Applications, ICSIPA 2021

作者： Altakrouri, Saleh Usman, Sahnius Bt Ahmad, Norulhusna Binti Justinia, Taghreed Noor, Norliza Mohd Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur Malaysia Jeddah College of Advertising University of Business and Technology Jeddah Saudi Arabia Technology and Health Informatics Department King Saud bin Abdulaziz University for Health Sciences Jeddah Saudi Arabia

ISBN: (纸本)9781665435925

image to image translation based on deep learning models is a subject of immense importance in the disciplines of Artificial Intelligence (AI) and Computer Vision (CV). A variety of traditional tasks such as image colorization, image denoising and image inpainting, are categorized as typical paired image translation tasks. In computer vision, super-resolution regeneration is particularly important field. We proposed an improved algorithm to mitigate the issues that arises during the reconstruction using super resolution based on generative adversarial network. It is difficult to train in reconstruction of results. The generated images and the corresponding ground-truth images should share the same fundamental structure in order to output the required resultant images. The shared basic structure between the input and the corresponding output image is not as optimal as assumed for paired image translation tasks, which can greatly impact the generating model performance. The traditional GAN based model used in image-to-image translation tasks used a pre-trained classification network. The pre-trained networks perform well on the classification tasks compared to image translation tasks because they were trained on features that contribute to better classification. We proposed the perceptual loss based efficient net Generative Adversarial Network (PLE-GAN) for super resolution tasks. Unlike other state of the art image translation models, the PL-E-GAN offers a generic architecture for image super-resolution tasks. PL-E-GAN is constituted of two convolutional neural networks (CNNs) that are the Generative network and Discriminator network Gn and Dn, respectively. PL-E-GAN employed both the generative adversarial loss and perceptual adversarial loss as objective function to the network. The integration of these loss function undergoes an adversarial training and both the networks Gn and Dn trains alternatively. The feasibility and benefits of the PL-E-GAN over several imag

关键词： Generative adversarial networks

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UAVs-Borne MIMO Radars Layout Optimization based on Clustered signal Integration

UAVs-Borne MIMO Radars Layout Optimization Based on Clustere...

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ieee international conference on signal and image Processing (ICSIP)

作者： Zhao Zhang Xiaolong Li Zhe Chen Mingxing Wang Longji Gao Zhi Sun School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

This paper considers the layout problem for the unmanned aerial vehicles (UAVs)-borne multi-input multi-output (MIMO) radar system in the situation of signal integration to improve the detection probability and radar effective coverage area (ECA). Because of the coherent constraints between UAV node echo signals, the signals can only be integrated coherently when the distances between nodes are relatively short; otherwise, the signals must be integrated incoherently. Thus, we propose a system model of the clustered UAV MIMO radar based on hybrid integration for the varying distances between the nodes. The model groups the system’s nodes into clusters, where the distance between nodes within the same cluster is small and the distance between nodes from different clusters is larger. A clustering criterion and an approximation of the ECA solution calculation are also included in the model. Then, we propose an optimization solution for radar node placement based on Cuckoo search-particle swarm optimization (CS-PSO). Finally, compared to CS-based, PSO-based, and random deployment methods, the simulation results examine the feasibility and effectiveness of the proposed method.

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