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Analyzing Radar Cross Section signatures at automotive microwave radar 3

Analyzing Radar Cross Section signatures at automotive micro...

引用

3rd international Computing Imaging conference, CITA 2023

作者： Wang, Zizheng Qi, Feng Wu, Hongming Xidian University Xi’an710126 China Shenyang Institute of Automation Chinese Academy of Sciences China The Key Laboratory of Liaoning Province in Terahertz Imaging and Sensing Shenyang110169 China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China The Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110169 China College of information science anf engineering Northeastern University Liaoning Province Shenyang110819 China

ISBN: (纸本)9781510671409

With the increasing demand for autonomous vehicles, the reliable detection and recognition of objects have become paramount. Millimeter-wave radar systems operating at frequencies such as 94GHz offer several advantages, including high resolution, precise range measurement, and resilience to adverse weather conditions. However, accurately characterizing the RCS of objects at this frequency range is essential to optimize the performance of these radar systems. In this work, we present Radar Cross Section (RCS) measurements of various objects commonly encountered on the road such as the metal balls, the bricks. The acquired findings illuminate the radar signal fluctuations demonstrated by different entities at a frequency of 94GHz. This knowledge serves as a foundation for developing robust algorithms and signal processing techniques that enhance the object detection, classification, and tracking capabilities of radar-based autonomous driving systems. Moreover, our research provides a valuable dataset of RCS measurements for different objects at 94GHz, which can serve as a benchmark for future studies in the field of radar system design and testing. The availability of such a dataset facilitates the development and validation of radar system models, the evaluation of sensor fusion approaches, and the comparison of performance across different radar hardware and signal processing algorithms. The results contribute to the advancement of radar-based autonomous driving systems by providing crucial insights into the scattering characteristics of objects at millimeter-wave frequencies. The findings have practical implications for optimizing the design, testing, and performance of radar systems, ultimately enhancing the safety and efficiency of autonomous vehicles in real-world scenarios. © 2023 SPIE.

关键词： Millimeter waves

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Simulation Method for Generating Phase Noise Based on Frequency Range Merging 7

Simulation Method for Generating Phase Noise Based on Freque...

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7th international conference on intelligent Computing and signal processing, ICSP 2022

作者： Sun, Tianwei Zhong, Xingwang Zong, Nianke Academy of Space Information Systems Xi'an China

ISBN: (纸本)9781665478571

As an important indicator to measure the stability of frequency standard sources such as crystal oscillators, phase noise is an important factor affecting the accuracy of electronic systems. In this paper, the common simulation methods of phase noise are summarized, and the method of frequency range merging is proposed. This method generates the power-law spectral noise of the divided frequency range through the FIR filter, and then merges it to generate the phase noise of the whole frequency range. Through the simulation verification in time domain and frequency domain, this method greatly reduces the time of generating simulation data, and improves the fitting degree of power spectral density of phase noise between the actual generated value and the theoretical value. In addition, this method reduces the difference between the actual standard deviation of phase noise and theoretical root mean square jitter to the magnitude of 10-4. In conclusion, this paper proposes an accurate and efficient simulation method of phase noise, which provides a basis for analyzing its effects. © 2022 ieee.

关键词： Merging

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Brain Tumor Segmentation from MRI Scans using 2D U-Net and other U-Net-Based Models

Brain Tumor Segmentation from MRI Scans using 2D U-Net and o...

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2023 international conference on Artificial Intelligence robotics, signal and Image processing, AIRoSIP 2023

作者： Magboo, Ma. Sheila A. Coronel, Andrei D. University of the Philippines Manila College of Arts and Sciences Department of Physical Sciences and Mathematics Manila Philippines Ateneo de Manila University School of Science and Engineering Department of Information Systems and Computer Science Quezon City Philippines

ISBN: (纸本)9798350327472

In this study four deep learning-based image segmentation models were constructed to segment brain tumors from FLAIR and T1ce MRI modalities. The four models were 2D U-Net, and the three 2D U-Net based variants which are the U-Net VGG19, U-Net ResNet50, and U-Net InceptionV3. Both qualitative and quantitative methods of assessment were used to evaluate model performance. Qualitative methods include visual presentation of ground truth vis-à-vis the predicted segmentation in order to see how similar is the prediction vis-à-vis the ground truth. As for the quantitative metrics Dice coefficients of the whole tumor, necrotic/core, edema, and enhancing tumors were obtained along with the Mean IOU, Precision, Sensitivity, and Specificity. Generally, all models achieved good performance however in this study 2D U-Net emerged as the best model for getting the highest scores in almost all metrics. Clinical decision-support tools like this can provide timely and accurate information to help radiologists come up with more informed decisions associated with assessment of the tumor's aggressiveness, potential interventions or therapies, and in monitoring of treatment response over time. © 2023 ieee.

关键词： Image segmentation

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Untargeted Backdoor Attack Against Object Detection 48

Untargeted Backdoor Attack Against Object Detection

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48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Luo, Chengxiao Li, Yiming Jiang, Yong Xia, Shu-Tao Tsinghua University Tsinghua Shenzhen International Graduate School China Research Center of Artificial Intelligence Peng Cheng Laboratory China

ISBN: (纸本)9781728163277

Recent studies revealed that deep neural networks (DNNs) are exposed to backdoor threats when training with third-party resources (such as training samples or backbones). The back-doored model has promising performance in predicting benign samples, whereas its predictions can be maliciously manipulated by adversaries based on activating its backdoors with pre-defined trigger patterns. Currently, most of the existing backdoor attacks were conducted on the image classification under the targeted manner. In this paper, we reveal that these threats could also happen in object detection, posing threatening risks to many mission-critical applications (e.g., pedestrian detection and intelligent surveillance systems). Specifically, we design a simple yet effective poison-only backdoor attack in an untargeted manner, based on task characteristics. We show that, once the backdoor is embedded into the target model by our attack, it can trick the model to lose detection of any object stamped with our trigger patterns. We conduct extensive experiments on the benchmark dataset, showing its effectiveness in both digital and physical-world settings and its resistance to potential defenses. © 2023 ieee.

关键词： AI Security Backdoor Attack Object Detection Physical Attack Trustworthy ML

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Research on the Application of Radio Frequency Sensing Technology in Obstacle Detection for Driverless Vehicles 6

Research on the Application of Radio Frequency Sensing Techn...

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6th ieee international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Zhang, Jialiang Cai, Zhaobo Wuhan University of Technology School of Information Engineering Wuhan China

ISBN: (纸本)9798350305623

In the swiftly evolving realm of intelligent Transportation systems, autonomous driving technology has emerged as a focal point of research, with the precise identification and detection of obstacles being a pivotal technology. Although significant strides have been made with traditional obstacle detection methods such as visual systems, radar, and ultrasonic sensors, challenges persist in complex environments. This study delves into the feasibility of applying Radio Frequency (RF) sensing technology, widely employed in indoor positioning and the Internet of Things, to obstacle detection in autonomous vehicles. Our research indicates that RF sensing technology markedly enhances detection rates and accuracy. The paper comprehensively analyzes the application of RF sensing in obstacle detection, proposing an efficient obstacle detection system based on this technology. Results suggest that this system offers more reliable and safer obstacle detection under varying environmental conditions. © 2023 ieee.

关键词： Autonomous Vehicles intelligent Transportation systems Obstacle Detection Radio Frequency Sensing Technology signal processing

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A Smart Model to Manage Traffic Using Infrared Sensors and Bell Detection System: A Computer Controlled Traffic System 2

A Smart Model to Manage Traffic Using Infrared Sensors and B...

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2nd ieee international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Saxena, Ashendra Kumar Adlin, Jebakumari S. College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India University Karnataka Bangalore India

ISBN: (纸本)9798350369960

The escalating challenge of traffic congestion in urban areas necessitates innovative solutions at the intersection of computer science and artificial intelligence. The main problem is in the outdated nature of conventional traffic signal systems, which function based on predetermined time intervals and fail to appropriately adjust to the ever-changing patterns of urban traffic. This study presents a sophisticated traffic management system that utilizes machine learning and data mining concepts to optimize real-time traffic signal regulation. The suggested system utilizes infrared sensors to constantly monitor the level of traffic congestion at crossings. The data is transmitted to an advanced microcontroller, which utilizes machine learning techniques to forecast traffic patterns and subsequently modify the timing of traffic signals. This approach enables a more seamless and adaptable control of traffic flows, resulting in a substantial decrease in congestion and enhancement of overall traffic efficiency compared to traditional systems. By incorporating computer vision techniques, the system's ability to detect and evaluate several traffic parameters, such as vehicle count, speed, and categorization, is improved. The data is essential for the machine learning models to make well-informed recommendations regarding the durations of traffic lights. Furthermore, the system integrates robotics components to physically manipulate and operate traffic light hardware, guaranteeing smooth functioning and immediate response to the computational decisions made by the microcontroller. This research not only addresses the immediate practical concerns of urban traffic management but also contributes to the broader field of AI and robotics in realworld applications. The synergy of data mining, machine learning, and computer vision within a robust traffic management system exemplifies the transformative potential of AI technologies in urban infrastructure management. © 2023 ieee.

关键词： Audio signal Detection Machine Learning Algorithms Real-Time Data processing Traffic Optimization Techniques

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Performance Enhancement of IRS-Assisted Communication systems Under Rayleigh Fading Channels 10

Performance Enhancement of IRS-Assisted Communication System...

引用

10th international conference on signal processing and Communication, ICSC 2025

作者： Chawla, Ravneet Bansal, Bajrang Jaypee Institute of Information Technology ECE Department Noida India

ISBN: (纸本)9798331511890

intelligent reflecting surface (IRS) is a key enabling technology that helps in modifying the propagation environment. In this paper, we investigate the performance of an optimized IRS based communication system in the presence of single eavesdropper considering Rayleigh fading. It is assumed that the channel state information (CSI) of the transmitter-to-IRS's and IRS-to-receiver's channels is perfectly known to the IRS. The simulation results demonstrate that proposed optimized IRS based communication system has a noticeable performance improvement as compared to the random IRS based communication system in terms of channel capacity and secrecy capacity. Furthermore, our results show secrecy performance improvement with increase in number of IRS reflecting elements. © 2025 ieee.

关键词： Fading channels

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Research on the Application of Image processing Technology in Vehicle License Plate Recognition 8

Research on the Application of Image Processing Technology i...

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8th international conference on intelligent Computing and signal processing, ICSP 2023

作者： Qiu, Caihua Ding, Feng Guangdong University of Science and Technology 99 Xihu Road Nancheng District Guangdong Dongguan523083 China Guangzhou Xinhua University 248 Yanjianxi Road Machong Town Guangdong Dongguan523133 China

ISBN: (纸本)9798350302455

With the development of artificial intelligence, image recognition technology has been applied to various aspects of life, such as license plate recognition. License plate recognition has many application scenarios in real life, but due to various interferences in daily life, the accuracy of license plate recognition is often affected. In order to improve the accuracy and efficiency of license plate recognition, we need to integrate image processing technology into license plate image recognition pre-processing technology, and use Gaussian filter, grayscale, binarization to complete license plate image pre-processing, so as to reduce the interference of objective conditions on license plate recognition efficiency. Through experiments, we know that using image recognition technology can reduce the computational power consumption of license plate recognition systems, improve image recognition efficiency and accuracy. © 2023 ieee.

关键词： Image recognition

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Network Traffic Co-Movement Assessment via Oriented Basis signal processing and Ensemble Decision Trees 27

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Mohebbi, Mohammad Reza Hassan Zada, Mohammad Javad Rostami-Shahrbabaki, Majid Doller, Mario Yamnenko, Iuliia University of Applied Sciences Josef Ressel Center Vision2Move Project Tirol Kufstein Austria University of Passau Department of Computer Science Germany Technical University of Munich Traffic Engineering and Control Germany National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Department of Electronic Devices and Systems Kyiv Ukraine

ISBN: (纸本)9798331505929

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost. © 2024 ieee.

关键词： Time series

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A multi-level and multi-agent collaborative control platform for city-scale virtual power plants based on a cloud-pipe-edge-end fusion architecture 4

A multi-level and multi-agent collaborative control platform...

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4th international signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Baorong, Zhou Jiangnan, Li Wenmeng, Zhao Tao, Wang Biao, Zhao Wenyuan, Zheng Guanglei, Huang Mingyu, Ou Electric Power Research Institute China Southern Grid Guangzhou510530 China Shenzhen Power Supply Bureau Co. Ltd. Shenzhen518000 China Tsinghua University Shenzhen International Graduate School Shenzhen518000 China

ISBN: (纸本)9798331528676

With the continuous expansion and complexity of urban power grids, traditional power control methods are difficult to meet the needs of multi-agent, multi-energy and multi-level in modern power grids. To address this problem, this paper proposes a multi-level and multi-agent collaborative control platform for urban-scale virtual power plants based on a cloud-pipe-edge-end fusion architecture. By introducing technologies such as cloud computing, edge computing and the Internet of Things, the platform designs a multi-level control architecture that includes functional modules such as physical information modeling, resource aggregation optimization, automatic power control and instruction decomposition. The innovation of the platform lies in its ability to enable the full process of adjustable resources on the load side of the virtual power plant to participate in the automatic generation control (AGC) and automatic voltage control (AVC) of the power grid, and integrates intelligent monitoring and traceability technology of electric carbon, providing real-time and efficient power control and carbon emission management. Through actual case analysis and verification, the platform is significantly superior to traditional systems in terms of supply and demand balance, response speed, energy efficiency optimization and carbon emission management, which improves the overall operation efficiency and stability of the power grid and provides strong technical support for the intelligent management of urban power grids. ©2024 ieee.

关键词： Cloud platforms

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