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Generation of Digital Reconstruction Radiation image and Display System Design

Generation of Digital Reconstruction Radiation Image and Dis...

引用

image Processing and Computer Applications (ICIPCA), ieee international conference on

作者： Jiaying Zhang Huifeng Kong Lili Cai Su Zhou School of Biomedical Engineering Guangzhou Xinhua University Guangzhou China

Digitally reconstructed radiography (DRR) plays an important role in the treatment planning system (TPS) and many medical imaging applications such as 2D / 3D image registration. DRR generation technology is one of the core technologies of TPS. In this paper, the DRR image is generated by ray projection method based on volume rendering, and the display interface is designed to visualize the results. The medical image data set is resampled by reading the file header information of the medical image data set and the simulated light source projection distance, data set image spacing and pixel size are calculated by establishing the relevant coordinate transformation function and interpolation function. Finally, the DRR image results are displayed on the interface designed by the QT framework. The similarity is measured between the DRR image generated by ray casting method and the DRR image obtained by clinical TPS. The average of root mean square error(RMSE) is 0.25,and the peak signal-to-noise ratio(PSNR) is greater than 30 decibels and the structural similarity image measurement(SSIM) is positive. The results can provide the core technology for the subsequent DRR image display system and 2D / 3D registration. As well, the display interface can normally display the three orthographic views and DRR results of CT sequence images. It can be seen that the DRR image generated by the algorithm used in this paper has the same quality as the DRR image in clinical application, which proves the effectiveness of the algorithm and the good imaging effect.

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Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text Recognition

Col-OLHTR: A Novel Framework for Multimodal Online Handwritt...

引用

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

作者： Chenyu Liu Jinshui Hu Baocai Yin Jia Pan Bing Yin Jun Du Qingfeng Liu University of Science and Technology of China Hefei China iFLYTEK Research Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Online Handwritten Text Recognition (OLHTR) has gained considerable attention for its diverse range of applications. Current approaches usually treat OLHTR as a sequence recognition task, employing either a single trajectory or image encoder, or multi-stream encoders, combined with a CTC or attention-based recognition decoder. However, these approaches face several drawbacks: 1) single encoders typically focus on either local trajectories or visual regions, lacking the ability to dynamically capture relevant global features in challenging cases; 2) multi-stream encoders, while more comprehensive, suffer from complex structures and increased inference costs. To tackle this, we propose a Collaborative learning-based OLHTR framework, called Col-OLHTR, that learns multimodal features during training while maintaining a single-stream inference process. Col-OLHTR consists of a trajectory encoder, a Point-to-Spatial Alignment (P2SA) module, and an attention-based decoder. The P2SA module is designed to learn image-level spatial features through trajectory-encoded features and 2D rotary position embeddings. During training, an additional image-stream encoder-decoder is collaboratively trained to provide supervision for P2SA features. At inference, the extra streams are discarded, and only the P2SA module is used and merged before the decoder, simplifying the process while preserving high performance. Extensive experimental results on several OLHTR benchmarks demonstrate the state-of-the-art (SOTA) performance, proving the effectiveness and robustness of our design.

关键词： Training Handwriting recognition Visualization image recognition Text recognition Speech recognition Trajectory Decoding Speech processing Streams

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Basic Geometric Relations, Parameters and Characteristics of Theory for Phantoming of SAR images

Basic Geometric Relations, Parameters and Characteristics of...

引用

international conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science

作者： Volodymyr Pavlikov Simeon Zhyla Pavlo Pozdniakov Denys Kolesnikov Hlib Cherepnin Pavlo Malashta Aerospace radioelectronic systems department National Aerospace University “Kharkiv Aviation Institute” Kharkiv Ukraine State Scientific and Research Institute of Cybersecurity Technologies and Information Protection SSSCIP of Ukraine Kyiv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

The basics of the theory of radio engineering systems for radio image phantomization (addition of non-existent elements) formed by synthetic aperture radars of space carriers are proposed and discussed. The theory is based on the achievements of the radio engineering systems theory in radiovision systems synthesis by remote sensing systems and special form radio signals synthesis, radio physics of radio waves and various environments interaction, etc. The study focuses on the analysis of geometric relations arising in the interaction system “synthetic aperture radar - phantomization radar” (SAR-PhR) and the parameters related to them. It is assumed that the synthetic aperture radar is located on board the spacecraft, and the phantomization radar is located on the Earth's ground. The geometric relations are used to determine the formulas for estimating the time of potential interaction in the SAR-PhR system, estimating the time of actual interaction, estimating the shape and approximate parameters of the sensing signal, etc. Parameter estimates were calculated using computer modeling, and the formulas for these parameters are presented in the paper.

关键词： Space vehicles Geometry Surface waves Shape Spaceborne radar Mathematical models Sensors Telecommunications signal synthesis Synthetic aperture radar

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A Novel Lightweight Block Cipher for Robust Data Protection in IoT Devices

A Novel Lightweight Block Cipher for Robust Data Protection ...

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Innovations in High Speed Communication and signal Processing (IHCSP), international conference on

作者： Manish Nagpal Umakanth S Prutha P Kulkarni K.S. Bhuvaneshwari Rajat Saini Srisathirapathy S Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka ENTC Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Mech Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Due to the weak processing power and energy constraint associated with IoT devices, Data security and privacy are major issues enormously important for the growing environment of IoT. Cryptographic method is incompatible with the internet of Things (IoT) as this concept is characterized by too high resource demand. In this work, a new lightweight block cypher, designed for the IoT devices is introduced which is supposed to provide proper protection to the data. There are advantages of the suggested cypher that reduces computational time, energy requirements and memory usage while promoting efficiency as well as high security. In order to avoid usual cryptographic attacks such as differential and linear, the cipher employs advanced encryption techniques together with an enhanced key scheduling mechanism. It is stated from experiments that the cypher is suitable for the implementation in energy-constrained IoT networks based on the fact that it attains an equilibrium between efficiency, security, and energy conservation. This work enhances data protection in IoT applications significantly but affords devices with limited power to avoid operating slower.

关键词： Ciphers Technological innovation Processor scheduling Memory management Data protection signal processing algorithms signal processing Encryption Computational efficiency internet of Things

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Comparative Analysis of Continuous Wavelet Transforms on Vibration signal in Bearing Fault Diagnosis of Induction Motor

Comparative Analysis of Continuous Wavelet Transforms on Vib...

引用

international conference on Electronics, Communications and Information technology (ICECIT)

作者： Toma, Rafia Nishat Toma, Farzana Haque Kim, Jong-Myon Univ Ulsan Dept Elect Elect & Comp Engn Ulsan South Korea BRAC Univ Dept Comp Sci Dhaka Bangladesh

ISBN: (纸本)9781665423632

Bearing failure is considered as one of the major problems in induction motor, which can result a huge mechanical damage if is not monitored from the initial stage. A complete fault classification method is presented in this paper by combining wavelet-based signal processing technique and deep learning method for fault classification. Vibration signal for eight different bearing conditions has been considered and initially, Hillbert Transform, and envelope spectrum are applied to extract the fault frequency information. With the fault frequency band, three different continuous wavelet transforms (Morse, Morkt, and Bump) are employed to generate the 2-D image of each bearing conditions. Each RGB image for different faulty conditions reflects the distinguishable pattern. Finally, a convolution neural network (CNN) is applied to classify the bearing faults by learning the discriminative features of the 2-D images. The CNN model is trained and test individually with the three different wavelet images and the classification accuracy is found more than 98% for each case. Additionally, two predefined CNN models (LeNet-5 and AlexNet) also applied to validate our proposed method for image classification. The experimental results indicates that the conversion of the 1-D vibration time domain signal into 2-D time-frequency image through wavelet and incorporate CNN model to classify the images can be an effective approach in the field of fault diagnosis.

关键词： bearing fault diagnosis CWT CNN envelope spectrum time-frequency analysis

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Liveness Score-based Regression Neural Networks for Face Anti-Spoofing

Liveness Score-Based Regression Neural Networks for Face Ant...

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

作者： Youngjun Kwak Minyoung Jung Hunjae Yoo JinHo Shin Changick Kim KakaoBank Corp. South Korea Department of Electrical Engineering KAIST South Korea AIRC Korea Electronics Technology Institute South Korea

Previous anti-spoofing methods have used either pseudo maps or user-defined labels, and the performance of each approach depends on the accuracy of the third party networks generating pseudo maps and the way in which the users define the labels. In this paper, we propose a liveness score-based regression network for overcoming the dependency on third party networks and users. First, we introduce a new labeling technique, called pseudo-discretized label encoding for generating discretized labels indicating the amount of information related to real images. Secondly, we suggest the expected liveness score based on a regression network for training the difference between the proposed supervision and the expected liveness score. Finally, extensive experiments were conducted on four face anti-spoofing benchmarks to verify our proposed method on both intra-and cross-dataset tests. The experimental results show our approach outperforms previous methods.

关键词： Training image coding Neural networks signal processing Benchmark testing Robustness Acoustics

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LLCNN-based image Enhancement with Squeeze and Excitation Network for Improved Visual Quality

LLCNN-based Image Enhancement with Squeeze and Excitation Ne...

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international conference on Computational Intelligence and Communication Networks (CICN)

作者： Maheswari Subburaj Thirumurugan Shanmugam Arun Kumar Sivaraman Janakiraman Nithiyanantham Priya Ravindran Babu Sivam School of CSE Vellore Institute of Technology Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman Digital Engineering Solution Centre Photon Interactive Inc. Ontario Canada Department of ECE K L N College of Engineering Tamilnadu India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

image enhancement, particularly in low-light conditions, has long been a research focus to improve visual quality. Low-light settings often cause visibility issues and detail loss, which recent deep-learning techniques have effectively addressed. This study explores the combination of a low-light convolutional neural network (LLCNN), a convolutional neural network designed explicitly for low-light image enhancement, with a Squeeze and Excitation Network (SEN), which recalibrate features to emphasise critical details and suppress noise. Integrating SEN into LLCNN enhances image details while reducing noise and artefacts typically present in low-light conditions. Experimental results show that this combined approach outperforms existing methods in objective metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), as well as visual quality.

关键词： Measurement Visualization PSNR Surveillance Noise Superresolution Merging Convolutional neural networks Reliability image enhancement

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Analysis of Electromagnetic Field Radiation From Synchronisation signal Block (SSB) in 5G systems

Analysis of Electromagnetic Field Radiation From Synchronisa...

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ieee international conference on Computer Communication and the internet (ICCCI)

作者： Keze Li Michael S. Mollel Olaoluwa Popoola Yusuf Sambo Muhammad Ali Imran James Watt School of Engineering University of Glasgow

The unprecedented data demand from new use cases such as extended virtual reality and holograms requires the development of new mobile generation architectures. The fifth generation and beyond (5G-Beyond) wireless technology aims to address this need by increasing the number of base stations per unit area and utilizing high-frequency bands such as millimetre and terahertz waves. The 5G Beyond promises to deliver high data rates, network reliability, and low latency. However, this comes at the cost of increased electromagnetic field exposure (EMF) in a given area. Although there is no evidence linking EMF from mobile communication systems to health hazards, more research needs to be carried out to better understand the interaction between EMF and the human body and keep an eye on the EMF level. This article presents the results of a study on the EMF power density caused by control signals, especially synchronisation signal block (SSB), which are transmitted periodically from a 5G mobile communication antenna. Furthermore, from the findings, it can be observed that the level of exposure to EMF caused by SSB is within the international guidelines; yet more research is needed to mitigate unnecessary SSB transmission to further reduce the level of EMF and minimize energy consumption. In general, the results show that the highest power density values were observed at the closest distances and angles to the antenna, yet the EMF exposure levels are still below the limit. The findings of the study can inform the development of guidelines and regulations for safe exposure to electromagnetic radiation from 5G technology and other sources.

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FastPlane: A Fully Convolutional Network for Real-time 3D Plane Segmentation

FastPlane: A Fully Convolutional Network for Real-time 3D Pl...

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ieee international conference on Consumer Electronics (ICCE)

作者： Haijun Zhang Qingji Ren Li Dong Kim Fung Tsang Department of Computer Science Harbin Institute of Technology Shenzhen P. R. China Shenzhen china The College of Big Data and Internet Shenzhen Technology University Shenzhen P. R. China School of Electrical and Computer Engineering Nanfang College Guangzhou Guangzhou P. R. China

3D plane segmentation is a challenging computer vision task that involves the detection of planar regions in a scene and the prediction of their spatial position. Recent methods have addressed this problem as a multi-task learning problem in which convolutional neural networks with separate branches predict the plane segmentation mask and the dense depth maps, or plane parameters. However, existing methods often rely on a multi-stage object detection framework for plane segmentation, resulting in slow inference speeds that hinder real-time applications. To address this issue, we propose FastPlane, a real-time 3D plane segmentation network based on a fully convolutional structure. FastPlane comprises a shared backbone, a fully convolutional plane segmentation network, and a lightweight depth estimation network. Given a single image as input, FastPlane outputs both the plane segmentation mask and dense depth maps. Extensive experiments on public datasets demonstrate the effectiveness and efficiency of FastPlane, outperforming existing methods in terms of plane segmentation and depth estimation metrics. Notably, FastPlane achieves an impressive inference speed of 45.0 FPS, making it suitable for real-time applications in robotics, augmented reality, and virtual reality.

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Enhanced Pedestrian Detection Model Transfer-Trained on YOLOv8 Using DenseFused RGB and FIR images

Enhanced Pedestrian Detection Model Transfer-Trained on YOLO...

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international conference on Smart Computing (SMARTCOMP)

作者： Arata Yoshihara Ismail Arai Arata Endo Masatoshi Kakiuchi Kazutoshi Fujikawa Internet Architecture and Systems Laboratory Nara Institute of Science and Technology Nara Japan Information Initiative Center Nara Institute of Science and Technology Nara Japan

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

There are broad benefits to developing pedestrian spaces in terms of environment, culture, and economy. Given this context, accurately measuring pedestrian traffic is considered a critical indicator of sidewalk usage. Currently, the mainstream method for detecting pedestrians utilizes RGB camera footage installed along sidewalks. However, especially during nighttime and adverse weather conditions, insufficient lighting hampers detection accuracy. In contrast, Far-Infrared(FIR) imaging does not require a light source as it measures radiated heat. This study proposes a pedestrian detection and tracking model that integrates the strengths of both RGB and FIR cameras through image fusion processing. Specifically, pedestrians are detected from fused images using a pedestrian detector and then tracked using a tracking system to measure pedestrian counts. Additionally, a version of the pedestrian detector trained on the fused images through transfer learning is developed, and its detection results are compared with those from a non-transfer learning model. The experimental results demonstrate that using fused images for detection and tracking is effective in specific data scenarios, confirming the utility of the image fusion model under varied conditions.

关键词： Pedestrians Accuracy Finite impulse response filters Current measurement Transfer learning Detectors Cameras

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