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Real-Time Obstacle Detection and Localization for USV Using Stereo Vision and Edge Computing

Real-Time Obstacle Detection and Localization for USV Using ...

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2024 international conference on Artificial Intelligence of Things and systems, AIoTSys 2024

作者： Wang, Di Chen, Haiming Hasan, Md Asif Zhang, Weihao Hu, Yuqing Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo Zhejiang315211 China Information Center Ningbo Polytechnic Ningbo Zhejiang315800 China Ningbo Industrial Internet Research Institute Ningbo Zhejiang315000 China

ISBN: (纸本)9798331507886

Unmanned Surface Vessel (USV) is widely used in various fields, and obstacle avoidance is a key technology for USV. Accurately detecting the position and type of obstacles is crucial for obstacle avoidance. Active sensors can accurately detect the position of obstacles but cannot distinguish their types, necessitating the use of vision sensors. Therefore, this paper employs stereo vision and proposes a stereo obstacle detection and localization method, based on SGBM and YOLOv5. The SGBM algorithm generates depth information, while the YOLOv5 algorithm detects the position of obstacles in the image. To address the issue of errors in measuring the continuous position of obstacles, this paper uses a Kalman filter to smooth the trajectory. Experimental results show that the proposed method improves obstacle localization accuracy by approximately 38% compared to raw measurements. Besides, considering the energy constraints and computing capabilities of lightweight USV, which cannot meet the demands of processing vision tasks, this paper introduces an edge-device collaboration architecture, by offloading the USV computing tasks to edge nodes, thereby extending the computing capacity and energy efficiency of the USV. Experimental results indicate that the proposed architecture meets the real-Time obstacle avoidance requirements of USV. © 2024 IEEE.

关键词： Stereo vision

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A Multilayer Community Detection Algorithm based on Aggregation in Social internet of Things 7

A Multilayer Community Detection Algorithm Based on Aggregat...

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7th international conference on Intelligent Computing and signal Processing, ICSP 2022

作者： Wang, Pingdong Victor Chan, Wai Kin Tsinghua-Berkeley Shenzhen Institute Intelligent Transportation and Logistics Systems Lab Tsinghua Sigs Shenzhen China

ISBN: (纸本)9781665478571

Social internet of Things (SIoT) is a new paradigm that combines internet of Things (IoT) and Social Network. SIoT makes the IoT smarter by analyzing the social relationships among IoT devices. SIoT can contribute the collaboration among devices belonging to a community to process local events without the support of a remote server. Community detection in SIoT requires attention to the multiple social relationships that exist between devices. However, the community structure in each of the multiple relationship layers is not considered in the existing studies. Hence, in this study, a Multilayer Community Detection Algorithm based on Aggregation was proposed to solve the abovementioned problem. The rationale of the algorithm is to first perform community detection for each layer of the network separately, and then merge or dissolve the overlapping communities based on their overlap rates. For the overlapping communities that are not merged or dissolved, they are partitioned into non-overlapping communities. The algorithm has been performed on real-world data collected from 16216 devices in Santander, Spain. The experimental results show that our algorithm performs better than the existing algorithms and the success rate and success rate per unit time are raised by 2%-5% and up to 15%, respectively. © 2022 IEEE.

关键词： internet of things

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Performance Evaluation of Fault-Tolerant Approximate Adder 6

Performance Evaluation of Fault-Tolerant Approximate Adder

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6th international conference on Devices, Circuits and systems, ICDCS 2022

作者： Mendez, Tanya Nayak, Subramanya G. Manipal Institute of Technology Manipal Academy of Higher Education Department of Electronics and Communication Engineering Manipal India

ISBN: (纸本)9781665480949

The main emphasis of fault-tolerant adders is to minimize the performance metrics area, power and delay. Error resilient applications like image processing, multimedia, and internet of Things (IoT) accept degradation in the results providing a wide range of prospects for approximate adder's optimization. Three designs of fault-tolerant adders Selector based Fault-Tolerant Adder-I (SBFTA-I), Selector based Fault- Tolerant Adder-II (SBFTA-II) and Optimized Fault-Tolerant Adder (OFTA) are proposed and implemented in this work with reduced switching activity and gate count. The proposed fault tolerant adders are further used in the design of 16-bit adders, where the upper 8-bits are realized using actual adders, and the lower 8 bits are implemented using the three proposed fault tolerant adder designs. The proposed fault-tolerant and existing approximate adders are synthesized in an electronic design automation (EDA) Tool using a 90 nm technology library. The proposed adder designs showed significantly improved performance metrics compared to the conventional techniques © 2022 IEEE.

关键词： Performance evaluation Fault tolerance image processing Fault tolerant systems Switches Logic gates Libraries

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A Novel Attention Heat Map-based Video Anomaly Detection Framework 10

A Novel Attention Heat Map-Based Video Anomaly Detection Fra...

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10th international conference on signal Processing and Integrated Networks, SPIN 2023

作者： Sharma, Preeti Gangadharappa, M. School of It Artificial Intelligence and Cyber Security Rashtriya Raksha University Gujarat India Electronics and Communication Netaji Subhash University of Technology East Campus Delhi India

ISBN: (纸本)9781665490993

In video surveillance systems for the purpose of public safety, automatic anomaly detection is a vital responsibility. The goal of anomalous activity recognition is to identify the frames with various spatial information. In the proposed methodology we presented a framework where we can classify and localize rare events by using trainable features only through the global average pooling layer rather than flattening the model, using the pre-trained model. This model is trained only on the global average pooling layer while the other layers are non-trainable. Extracted features are further connected to dense layers to make predictions as anomalies or normal images. We first determined all images classified as anomalies by the model to localize anomalies and save those images. Using a heat map with peak value and threshold we locate different anomalies in the image using weights of the last two layers of the trained model. Our method adds the advantage of both frame-level and pixel-level performance where we classify and locate the anomalies. We evaluated our network performance on two standard datasets that outperform the state of art methods by achieving an accuracy of 93.7% for UCSD ped1 and 95.4% for ped2 datasets respectively. © 2023 IEEE.

关键词： image segmentation

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Compressive Sensing Solution Optimization Method in Sensing-Transmission-Calculation Integrated System 24

Compressive Sensing Solution Optimization Method in Sensing-...

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24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on Data Science and systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Ma, Li Zhang, Zelin Li, Yang Fu, Yingxun Ma, Dongchao North China University of Technology Beijing China

ISBN: (纸本)9798350319934

The internet of Everything is a necessary link for building smart cities, smart manufacturing, and other scenarios. And the demand for many forms of data such as high definition and high precision for the internet of Everything puts great pressure on the storage and transmission of data. Starting from the end of the internet of Things (IoT), this paper investigates a compressed sensing algorithm adapted to the end of the IoT sensing and computing system to reduce the power consumption and data communication volume of the end sensing and computing system and proposes an optimization algorithm for iteratively updating the measurement matrix and sparse dictionary. First, fix the sparse dictionary and use adaptive gradient descent to make the Gram matrix infinitely approximate the unit matrix and the matrix determined by the sparse dictionary structure to obtain the optimized measurement matrix;then, use the sample data obtained from this matrix to perform sparse dictionary learning and use the results as the dictionary input for the next round of measurement matrix;finally, improve the measurement matrix and dictionary performance through continuous iterative updating. Simulation experiments show that the measurement matrix and sparse dictionary obtained by this method have better performance for image signal acquisition reconstruction in a low sampling rate environment compared with traditional optimization algorithms. © 2022 IEEE.

关键词： internet of things

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Weighted Non-Convex Penalty Minimization for Compressed Ultrasound signal Reconstruction 13

Weighted Non-Convex Penalty Minimization for Compressed Ultr...

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13th IEEE international conference on CYBER technology in Automation, Control, and Intelligent systems, CYBER 2023

作者： Fu, Xiaoyan Zhou, Lijuan Li, Chuanzhong Zhang, Miaomiao Li, Wenling College of Information Engineering Capital Normal University Beijing China School of Cyberspace Security Hainan University Haikou China School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350315196

The reconstruction of high quality images from a limited number of radio-frequency (RF) measurements is crucial for ultrafast plane wave ultrasound (PWUS) imaging system. The sparse and low rank model can efficiently exploit the correlation between different ultrasound channels. However, the reconstruction strategies derived from sparse models use convex relaxations of the matrix l1 norm to induce sparsity, and convex approximations are widely known to result in biased estimators, which usually underestimates the coefficient amplitudes of RF signals in the frequency domain. In this work, we designed a weighted nonconvex penalty (WNCP) to extract sparsity more strongly than the l1 norm to alleviate the bias issue, aiming at reducing reconstruction errors. An optimization algorithm based on simultaneous direction method of multipliers (SDMM) is proposed to evaluate the effectiveness through in vivo data from PICMUS dataset. Results show the proposed method obtains a B-mode image quality superior to that obtained through our previous method. © 2023 IEEE.

关键词： Ultrasonics

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Exploiting Richness of Learned Compressed Representation of images for Semantic Segmentation

Exploiting Richness of Learned Compressed Representation of ...

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2023 IEEE international conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Kakaiya, Ravi Sathish, Rakshith Sheet, Debdoot Sethuraman, Ramananthan Indian Institute of Technology Kharagpur West Bengal India Intel Technology India Pvt. Ltd. Bengaluru India

ISBN: (纸本)9798350313154

Autonomous vehicles and Advanced Driving Assistance systems (ADAS) have the potential to radically change the way we travel. Many such of such vehicles currently rely on segmentation and object detection algorithms to detect and track objects around its surrounding. The data collected from the vehicles are often sent to cloud servers to facilitate continual/life-long learning of these algorithms. Considering the bandwidth constraints, the data is compressed before sending it to servers, where it is typically decompressed for training and analysis. In this work, we propose the use of a learning-based compression Codec to reduce the overhead in latency incurred for the decompression operation in the standard pipeline. We demonstrate that the learned compressed representation can also be used to perform tasks like semantic segmentation in addition to decompression to obtain the images. We experimentally validate the proposed pipeline on the Cityscapes dataset, where we achieve a compression factor up to 66× while preserving the information required to perform segmentation with a dice coefficient of 0.84 as compared to 0.88 achieved using decompressed images while reducing the overall compute by 11%. © 2023 IEEE.

关键词： image compression

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AIoTC 2022 - Proceedings of the 2022 international conference on Artificial Intelligence, internet of Things and Cloud Computing technology

AIoTC 2022 - Proceedings of the 2022 International Conferenc...

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2022 international conference on Artificial Intelligence, internet of Things and Cloud Computing technology, AIoTC 2022

The proceedings contain 15 papers. The topics discussed include: low complexity joint super-resolution algorithm for range azimuth of TDM-MIMO LFMCW radar;coupled feedback attention networks;EEG source imaging method improves the detection performance of emergency braking intention in man-machine hybrid driving;research on integrated technology based on a wind turbine;design of precision configurable multiply accumulate unit for neural network accelerator;research on CT image classification algorithm of COVID-19 based on improved ResNet;topic extraction based on LDA and its application in tourism;power tower tilt monitoring method based on Beidou and improved Yolov3;parameter identification of the input nonlinear systems with the colored noise;and blockchain-based charitable donation privacy protection scheme.

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Hybrid Deep Learning for Multi-Modal image Fusion: Elevating Brain Tumor Detection 2

Hybrid Deep Learning for Multi-Modal Image Fusion: Elevating...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Mehta, Shiva Singh, Amanveer Chitkara University Institute of Engineering and Technology Centre for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798350366570

This work presents a new dual deep learning framework that incorporates CNNs and ViTs into the multi-modal medical picture fusion to enhance the diagnostic accuracy of the diagnosed brain tumour. This integration concerns MRI and PET information, applying the ability of CNNs to handle local spatial information and ViTs in collecting long-range relationships. Here, to test the efficacy of the proposed model, a publicly available multi-modal brain tumour database was used, where significant performance improvements were achieved over the conventional techniques. The developed model achieved a sensitivity of 96.8%, while the traditional CNN-based methods attained a sensitivity of only 91.2%. In addition, the research used it to achieve a high accuracy rate of 95.7 percent, a recall rate of 95.1 percent, and an F1 score of 95.4 percent for infallibility in locating tumors with minimal false positives and negatives. It was realized that the model in this study provided improved picture quality, with an SSIM of 0.89 compared to the 0.82 of earlier fusion techniques. The pilot qualitative study confirmed that the work of the fused pictures showed a better combination of PET and MRI information by merging spatial features with metabolic data to help distinguish the tumour's boundaries more clearly and identify small or irregular form tumours. The proposed CNN-ViT model outperforms other methods for detecting tumors with high levels of accuracy and high-quality images that give medicine a vital tool. The methodology has high potential to be applied to other medical imaging jobs as well. © 2024 IEEE.

关键词： Medical imaging

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8th international conference on signal and Information Processing, Network and Computers, ICSINC 2021

8th International Conference on Signal and Information Proce...

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8th international conference on signal and Information Processing, Network and Computers, ICSINC 2021

ISBN: (纸本)9789811933868

The proceedings contain 178 papers. The special focus in this conference is on signal and Information Processing, Network and Computers. The topics include: Research on Real-Time Monitoring Method of Spot Beam Antenna Direction on Satellites;A Novel SOC Estimation Approach for the Lithium-Ion Battery Pack Using in the Deep Space Landers;Analysis of Characteristics and Requirements of Large LEO Constellations Management;research on Space Project systems Technological Risk Assessment based on Multi-level Fuzzy Comprehensive Evaluation;research on the System Construction of Spaceborne Adaptive Software and Its Key Technologies;air-Space-Ground Integrated Information Network: technology, Development and Prospect;high-Resolution Satellite Radiation Big Data All-Statistical Intelligent Screening technology;EMC System Control on XX-2 Satellite;transmission and Control of the image Data from Remote Sensing Satellite to the Ground Station;micro-vibration Attenuation Design, Analysis and Verification for Agile Remote Sensing Satellite;LDPC Coding and Decoding Algorithm of Satellite Communication in High Dynamic Environment;Practice and Research on FMEA of Telecommunication Satellite System;characterization of On-Orbit Temperature and Pressure Performance for Hydrazine Propulsion System;On-Orbit Calibration of a 7-DOF Space Arm;correlation Analysis and Forecast of Electric Vehicle Charging Stations Considering Seasonal Factors;Inversion Modeling Study of Dissolved Oxygen in Wangyu River based on BP Neural Network;research on Satellite Energy Autonomous Security Strategy;Research and Development of High Speed and Large Bandwidth MIMO Data Transmission Test System;Continuous Evolution for Efficient Neural Architecture Search based on Improved NSGA-III Algorithm;research on Individual Identification of Radar Emitter based on Multi-domain Features.

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