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Properties of 5G Video Conference Traffic and Short-Term Forecasting of Input Data Rate

Properties of 5G Video Conference Traffic and Short-Term For...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Kutuzov, Denis Osovsky, Alexey Starov, Dmitriy Astrakhan State Technical Universitet Dept. of Communication Astrakhan Russia Astrakhan State Technical Universitet Dept. of Automated Information Processing and Control Systems Astrakhan Russia

ISBN: (纸本)9798350395044

In this article, we outlined the goals and prospects for short- and long-term data traffic forecasting in 5G networks. The study provides an overview of current traffic forecasting methods. A large number of works indicate a variety of approaches to the analysis and forecasting of mobile traffic. These approaches include both traditional statistical methods and deep learning methods, which is an important factor for the development of network technologies and optimization of radio access networks. Using mobile traffic data from Google Meet, MS Teams and Zoom video conferencing systems as an example, we studied the features of this type of traffic and came to the conclusion that the distribution of time intervals between the arrival of packets corresponds to an a-stable distribution. Having studied some machine learning methods designed for time series forecasting, we performed a comparative analysis of the effectiveness of these methods for short-term forecasting of data traffic intensity. © 2024 IEEE.

关键词： Forecasting

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Simulation Model for controlling the processing of information Packets by a NoC Router 7

Simulation Model for Controlling the Processing of Informati...

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7th International Conference on information, control, and Communication Technologies, ICCT 2023

作者： Kutuzov, Denis Osovsky, Alexey Starov, Dmitriy Maltseva, Nataliya Esaulenko, Vladimir Astrakhan State Technical Universitet Dept. of Communication Astrakhan Russia Astrakhan State Technical Universitet Dept. of Automated Information Processing and Control Systems Astrakhan Russia

ISBN: (纸本)9798350340945

This article describes the challenges of modeling switch fabric traffic that is part of NoC routers. Simulation is often used to validate a NoC design and identify potential performance bottlenecks before implementing hardware. Due to the distributed and complex network topology of routers, arbitration and routing algorithms, etc., timing, buffer queuing, and hence infrastructure performance are difficult to predict. Network traffic also generates application traffic in data processing nodes. When processing NoC traffic, the activity of IP cores is unstable and does not have stationarity, so it is very important to find traffic control mechanisms. Often, well-known models built on the basis of queuing theory do not always correctly reflect the connection request processing logic, do not take into account deadlocks and other features of the system functioning. An approach that can more accurately take into account the nuances of the switch is to build an agent-based model. To create it, we used the Mesa framework implemented in the Python programming language. The switch has small built-in buffers - one for each input. The model used a switch with 5 inputs and 5 outputs. The idea behind switch traffic management is to count how often there are transacts in the internal buffers that are directed to one exit. If they occur frequently enough, then the information flow is split and two or more outputs are given the same identifier to service such transacts in parallel. © 2023 IEEE.

关键词： Network-on-chip

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What the Cluster: An Unsupervised Framework to Segment Automotive Doppler Radar Pointclouds 18

What the Cluster: An Unsupervised Framework to Segment Autom...

引用

18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Eberhardt, Tim Dieter Bruhl, Tim Schwager, Robin Sohn, Tin Stribor Stork, Wilhelm Dr. Ing. H.c. F. Porsche Ag Dept. of Highly Automated Driving Systems Weissach Germany Institute for Information Processing Technologies Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350387278

The accurate identification of clusters from a radar point cloud is crucial for the safe operation of current Advanced Driver Assistance systems (ADAS). Robust clustering of point clouds enables the correct identification and localization of other safety-critical traffic participants. Particularly, the case of undetected traffic participants presents an unsafe and dangerous situation. This paper introduces the Layered Doppler-Position Clustering (LDPC) framework for this task. In the first layer, static and dynamic detections are separated considering the translational and rotational movement of the vehicle. In the second layer, pre-clusters are developed based on one-dimensional hierarchical clustering using the Doppler velocity. In the final layer, each pre-cluster undergoes further clustering using DBSCAN and the position data. By separating the process into layers, LDPC effectively combines Doppler velocity and position data for clustering. Combining both variables with different units, such as meters and meters per second, in a distance function, violates the semantic integrity of the data. Furthermore, the experimental results shown in this paper prove the applicability of the proposed LDPC framework. In various driving situations, the accuracy of the clustering results can be improved compared to existing methods. Additionally, we consider the safety-critical limits concerning specific radar sensor errors. Across all validation data, we achieved higher accuracy compared to existing systems in our experimental results. © 2024 IEEE.

关键词： Advanced driver assistance systems

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A Taxonomy and Survey on Sensor Blockage-Induced Degradation and its Impact on Advanced Driver Assistance systems 18

A Taxonomy and Survey on Sensor Blockage-Induced Degradation...

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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

ISBN: (纸本)9798350387278

Advanced Driver Assistance systems (ADAS) rely extensively on sensors to perceive and interpret their environment, enabling safe and efficient navigation, object detection or other functionalities. However, sensor blockage due to environmental contaminants such as dirt, rain, or snow poses a significant challenge, impacting sensor performance and consequently ADAS reliability. This paper presents a comprehensive taxonomy and survey on sensor blockage in ADAS, focusing on cameras, radars, and lidar sensors. The survey begins with an taxonomy overview of sensor principles used in ADAS and their vulnerabilities to blockage. Cameras, based on geometric optics, are susceptible to opaque contaminants like mud and transparent ones like raindrops. Advanced studies in computer vision and deep learning techniques are discussed for detecting and mitigating these contaminants. Radar systems, operating on electromagnetic waves, face blockage challenges from snow and water, affecting their range and accuracy. Detection algorithms and impact analyses highlight methods to mitigate these effects, crucial for maintaining radar reliability in adverse weather conditions. Lidar sensors, using laser pulses for precise distance measurement, are vulnerable to dust and moisture accumulation. Detection strategies such as total internal reflection and deep neural networks are reviewed, emphasizing advancements in contamination identification and cleaning mechanisms. The paper concludes with insights into the overall impact of sensor blockage on ADAS, stressing the need for robust detection algorithms and standardized testing protocols. © 2024 IEEE.

关键词： Advanced driver assistance systems

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Ant Colony Algorithm for Placing Electronic Components on a Printed Circuit Board, Taking into Account the Thermal Compatibility Criterion 16

Ant Colony Algorithm for Placing Electronic Components on a ...

引用

16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument Engineering, APEIE 2023

作者： Suzdaltsev, Ilya V. Suzdaltsev, Vladimir A. Bogula, Nelli Y. Dept. of Computer-aided Design System Kazan National Research Technical University named after A.N. Tupolev Kazan Russia Dept. of Automated Information Processing and Control Systems Kazan National Research Technical University named after A.N. Tupolev Kazan Russia

ISBN: (纸本)9798350330885

The problem of electronic components placing on a printed circuit board of an electronic device is formulated in this paper. The choice of constraints and criteria for the optimization problem is validated. We propose to take into account the thermal compatibility criterion to ensure normal thermal conditions of the printed circuit board. An indicator of the problem solution quality is the maximum temperature of a component among all components placed on a printed circuit board. The temperature of the components is determined based on the solution of the heat balance equation. The ant algorithm for placement problem solving is described. The sequence of vertices of the decision graph in the path formed by the ant colony determines the sequence of elements placement on the mounting plane of the printed circuit board and thus corresponds to the placement problem solution. The decrease in the maximum temperature of the printed circuit board temperature field was assessed depending on the number of the stage of constructing the best path by a ant colony, with different thermal conductivity between the components and the surrounding air environment. The results of computational experiments for effectiveness study of the ant algorithm are presented. The experiments carried out showed the effectiveness of the applied ant algorithm and that rational placement of the electronic components on the PCB makes it possible to reduce the maximum temperature of the components. © 2023 IEEE.

关键词： Printed circuit boards

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Properties of 5G Video Conference Traffic and Short-Term Forecasting of Input Data Rate

Properties of 5G Video Conference Traffic and Short-Term For...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Denis Kutuzov Alexey Osovsky Dmitriy Starov dept. of Communication Astrakhan State Technical Universitet Astrakhan Russia dept. of Automated Information Processing and Control Systems Astrakhan State Technical Universitet Astrakhan Russia

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Simulation Model for controlling the processing of information Packets by a NoC Router

Simulation Model for Controlling the Processing of Informati...

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information, control, and Communication Technologies (ICCT), International Conference on

作者： Denis Kutuzov Alexey Osovsky Dmitriy Starov Nataliya Maltseva Vladimir Esaulenko dept. of Communication Astrakhan State Technical Universitet Astrakhan Russia dept. of Automated Information Processing and Control Systems Astrakhan State Technical Universitet Astrakhan Russia

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Clarity Amidst Blur: A Deterministic Method for Synthetic Generation of Water Droplets on Camera Lenses

Clarity Amidst Blur: A Deterministic Method for Synthetic Ge...

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Tim Dieter Eberhardt Tim Brühl Robin Schwager Tin Stribor Sohn Wilhelm Stork Dept. of Highly Automated Driving Systems - Dr. Ing. h.c. F. Porsche AG Weissach Germany Institute for Information Processing Technologies (ITIV) - KIT Karlsruhe Germany

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

In computer vision, image clarity is crucial, particularly when challenges like water droplets on camera lenses can significantly impair the accurate analysis of visual data. While existing methods mainly focus on small droplets, the impact of larger droplets has been largely overlooked. This paper introduces a novel approach that models water droplets using randomly generated points and Bézier curves to simulate their shape on the lens. Based on this geometric framework, we developed a classifier to distinguish between two visual scenarios within the droplet. For larger droplets, we use a heuristic method to simulate various lens block-ages. We evaluate this simulation framework using a real stereo dataset from [19] with clear and soiled images. Our method, evaluated using Mean Squared Error (MSE) and Structural Similarity Index Measure (SSIM), demonstrates superior performance in MSE, while also achieving com-petitive results in SSIM compared to existing techniques for generating realistic water droplets. Additionally, we applied this technique for data augmentation in object detection tasks using the YOLOv7 [25] model. The results show improved robustness, especially in challenging conditions where large droplets obstruct the lens.

关键词： Visualization Computer vision Computational modeling Surveillance Cameras Data augmentation Data models Water pollution Standards Lenses

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Beyond the Smudge: Simulating Opaque and Transparent Automotive Camera Lens Soiling

Beyond the Smudge: Simulating Opaque and Transparent Automot...

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Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES), IEEE Symposia on

作者： Tim Dieter Eberhardt Tim Brühl Robin Schwager Tin Stribor Sohn Wilhelm Stork Dept. of Highly Automated Driving Systems Dr. Ing. h.c. F. Porsche AG Weissach Germany Institute for Information Processing Technologies Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798331508258

ISBN: (纸本)9798331508265

The performance and safety of Advanced Driver Assistance systems (ADAS) are critically dependent on the integrity of camera-based vision systems. Environmental contamination of the camera lens, such as mud, dirt, and dust, can severely degrade image quality, leading to compromised system reliability. Real-world collection of contaminated image data is expensive and infrequent, necessitating robust simulation techniques to emulate lens soiling for ADAS model training. In this paper, we present a novel augmentation technique that simulates opaque and transparent lens contamination using Perlin noise. Our proposed method employs a lightweight Convolutional Neural Network (CNN) to generate contamination shapes, effectively modeling natural patterns observed in real-world datasets. The simulation of contamination scenarios enhances ADAS robustness under challenging conditions, contributing to improved safety. Experimental results demonstrate the effectiveness of the method in replicating realistic contamination effects, with two key heuristics quantifying information loss due to lens soiling. This work provides a foundation for future research in camera degradation simulation for autonomous systems.

关键词： Degradation Training Pollution Shape Cameras Robustness Safety Convolutional neural networks Contamination Lenses

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A Taxonomy and Survey on Sensor Blockage-Induced Degradation and its Impact on Advanced Driver Assistance systems

A Taxonomy and Survey on Sensor Blockage-Induced Degradation...

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IEEE International Conference on Vehicular Electronics and Safety (ICVES)

ISBN: (数字)9798350387278

ISBN: (纸本)9798350387285

关键词： Surveys Laser radar Taxonomy Radar Benchmark testing Cameras Sensor systems Sensors Detection algorithms Contamination

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