2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)最新文献_第3页

Neural Network Based Analysis of Terahertz Frequency Signal Propagation for B5G/6G Wireless Networks 基于神经网络的B5G/6G无线网络太赫兹信号传播分析

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914236

Djamila Talbi, Mohamed Amine Korteby, Zoltán Gál

引用次数: 1

Greedy algorithm for edge-based nested community detection 基于边缘嵌套社区检测的贪心算法

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914051

Imre Gera, András London, András Pluhár

引用次数: 1

Fractals and Wavelets Based Energy Analysis of Cost-Balanced LEACH Sensor Network 基于分形和小波的成本平衡LEACH传感器网络能量分析

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914383

Mohamed Amine Korteby, Djamila Talbi, Zoltán Gál

引用次数: 0

Acoustic sensor with four microphones for a network used in monitoring 带有四个麦克风的声学传感器，用于监测网络

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914358

O. Pop, C. Rusu

引用次数: 0

Deep learning-based anomaly detection for imaging in autonomous vehicles 基于深度学习的自动驾驶汽车成像异常检测

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914092

Tibor Péter Kapusi, Laszlo Kovacs, A. Hajdu

{"title":"Deep learning-based anomaly detection for imaging in autonomous vehicles","authors":"Tibor Péter Kapusi, Laszlo Kovacs, A. Hajdu","doi":"10.1109/CITDS54976.2022.9914092","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914092","url":null,"abstract":"Autonomous driving and self-driven vehicles have become among the most pursued research areas in recent years. Nowadays, various driving tasks can be solved by applying the newest machine learning techniques such as line tracking, traffic sign recognition, automated speed adjustment, and parking. However, difficult visual conditions and anomalies can cause problems in selected algorithms, which may occur unexcepted and failure operations in these cases. It is also expected not just very expensive to do such kinds of experiments, but these problematic conditions are also lead to dangerous traffic situations at the same time. We made an effort to put these kinds of studies into a cost-effective and safe model-scale environment. This paper introduces an anomaly detection method capable of recognizing abnormal and burnt-out objects in image scenes. Our proposed method is based on a fast neural network architecture using YOLO layers to detect regions. Our experiments demonstrate the capabilities and detection accuracy of the designed neural network, called anomalyNet, with the complete training and evaluation process. In the study, we work with publicly available datasets, but our model-sized track and DAVE (University of Debrecen Autonomous VehiclE) play an important role also.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126217099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting the direction of the oil price trend using sentiment analysis 利用情绪分析预测油价走势方向

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914158

Róbert Lakatos, G. Bogacsovics, A. Hajdu

引用次数: 1

Sensor design and integration into small sized autonomous vehicle 小型自动驾驶汽车传感器设计与集成

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914037

László Kovács, Dávid Baranyai, Tamás Girászi, T. Majoros, Ádám Kovács, Máté Vágner, Dénes Palkovics, T. Bérczes

{"title":"Sensor design and integration into small sized autonomous vehicle","authors":"László Kovács, Dávid Baranyai, Tamás Girászi, T. Majoros, Ádám Kovács, Máté Vágner, Dénes Palkovics, T. Bérczes","doi":"10.1109/CITDS54976.2022.9914037","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914037","url":null,"abstract":"Autonomous vehicles use several different kinds of sensors to get information about the surrounding area. With sensors and artificial intelligence, the autonomous vehicle tries to find the optimal decision as close as possible to the appropriate behavior. Because of the huge amount of data, the usage of modern machine learning and data-driven approaches is necessary. Although computing big data is not easily handled especially onboard a vehicle, the critical mass of the diverse data generated from different sources is essential. In the field of autonomous vehicles, there have not been standards yet, but the range of applied sensors is well-known. Most systems use a combination of cameras, radar, and LIDAR (Light Detection and Ranging) sensors that transmit data to a central computer that detects the environment around the car. Self-driving development could be supported with model-sized self-driving vehicles because of the complexity of the area. The development of autonomous vehicles consists of security, communication, and data processing issues. Mistakes are increasing the risks of potential accidents. The realistic environment which can be simulated or built makes it possible that the learned behavior can be carried across the platforms while the differences in the sizes are not playing an important role in the matter of learning. The previous reason causes the model-size self-driving development to be more cost-effective. In our work, we developed a self-driving model car with different types of sensors. Measurement data from them can be used to improve the self-driving capabilities of the vehicle.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131280884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image sensor based steering signal for a digital actuator system 基于图像传感器的数字舵机系统转向信号

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914116

Arpad Pandy, Dávid-Gyula Kun, Laszlo Kovacs, Gábor Vasváry, Zoltán Pánti, A. Hajdu

引用次数: 1

Towards an in-network GPU-accelerated packet processing framework 一个网络内gpu加速包处理框架

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/CITDS54976.2022.9914271

Péter Vörös, Dávid Kis, P. Hudoba, Gergely Pongrácz, S. Laki

{"title":"Towards an in-network GPU-accelerated packet processing framework","authors":"Péter Vörös, Dávid Kis, P. Hudoba, Gergely Pongrácz, S. Laki","doi":"10.1109/CITDS54976.2022.9914271","DOIUrl":"https://doi.org/10.1109/CITDS54976.2022.9914271","url":null,"abstract":"Software-defined networking and data-plane programmability have opened up the possibilities for switches to be used for novel applications that are different than simple packet forwarding. Various tasks from low-level robot control to signal and data processing can be offloaded to network devices. In the past years, solutions exploiting programmable switching ASIC, FPGA or the combination of both have emerged. In this paper, we propose a GPU-accelerated switch design for supporting payload processing tasks in the network. The proposed design combines the processing capabilities of GPUs and the kernel-bypass library DPDK. We define different image processing use cases that can benefit from in-network computing, allowing execution without the need for an external server. The proposed method cannot only make the overall system performance better, but also reduce the power consumption since it requires less hardware elements. We evaluate and compare three models: Traditional external server with GPU in the local network, DPDK accelerated version of the previous model and the proposed GPU-accelerated in-network computing switch model. We investigate several benchmarks including both component-level and system-wide analysis. The examined use cases are related to video stream processing tasks like box blurring, Gaussian blurring and edge detection, demonstrating the performance improvement of our proposed design.","PeriodicalId":271992,"journal":{"name":"2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124587919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CITDS 2022 Cover Page CITDS 2022封面页

2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS) Pub Date : 2022-05-16 DOI: 10.1109/citds54976.2022.9914195

引用次数: 0