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2022 21st International Symposium on Communications and Information Technologies (ISCIT)最新文献

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Tri-Branch GAN: A Semi-supervised Method Based on Rebalance 三分支GAN:基于再平衡的半监督方法
Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931307
For deep learning applications in industrial scenarios, few-shot and imbalanced available datasets are very common. Traditional methods usually adopt the idea of hierarchical training, semi-supervised learning and the rebalancing method to train the model respectively, which has certain limitations: Separate trainings does not fully exploit the correlation between the two problems and causes additional computational overhead. Therefore, this paper proposes a semi-supervised learning method based on rebalance, named as Tri-branch GAN (Generative Adversarial Networks). This method makes full use of the correlation between the two problems, avoids the updating coating problem after the model parameter training, and saves the computational cost. Simulation results show that the proposed method can effectively improve the classification accuracy.
对于工业场景中的深度学习应用,可用数据集少、不平衡是非常常见的。传统方法通常采用分层训练、半监督学习和再平衡方法分别对模型进行训练,这种方法有一定的局限性:单独训练不能充分挖掘两个问题之间的相关性,会造成额外的计算开销。因此,本文提出了一种基于再平衡的半监督学习方法,称为三分支GAN (Generative Adversarial Networks,生成对抗网络)。该方法充分利用了两个问题之间的相关性,避免了模型参数训练后的更新涂层问题,节省了计算成本。仿真结果表明,该方法能有效提高分类精度。
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引用次数: 0
Performance Analysis of ADS-B Receiving System Based on LEO Satellite Constellation 基于LEO卫星星座的ADS-B接收系统性能分析
Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931245
Automatic Dependent Surveillance-Broadcast (ADS-B) signal reception based on low-orbit satellite constellation system has gradually become an important technical method of airspace traffic management. In this paper, by establishing the satellite receiving ADS- B system model, the relationship between the system performance and the ADS-B receiver sensitivity and other factors is theoretically analyzed. Theoretical analysis and simulation show that the terminal position update rate of the ADS- B receiving system based on the low-orbit satellite constellation is mainly determined by the receiving performance of the ADS-B receiver, the number of overlapping coverage of the constellation system and the number of terminals within the beam coverage; When the frame performance is better than 1×10-1, increasing the overlap coverage of the constellation system is the most effective way to improve the system capacity.
基于低轨卫星星座系统的广播自动相关监视(ADS-B)信号接收已逐渐成为空域交通管理的一种重要技术手段。本文通过建立卫星接收ADS-B系统模型,从理论上分析了系统性能与ADS-B接收机灵敏度等因素之间的关系。理论分析和仿真结果表明,基于低轨卫星星座的ADS-B接收系统的终端位置更新速率主要取决于ADS-B接收机的接收性能、星座系统的重叠覆盖数和波束覆盖范围内的终端数;当帧性能优于1×10-1时,增加星座系统的重叠覆盖是提高系统容量的最有效途径。
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引用次数: 4
Proposal of Single Input BSS Method Using QMF for Pulse Wave Extraction 基于QMF的单输入BSS脉冲波提取方法的提出
Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931304
Recently, a study of Pulse Wave(PW) extraction methods using blind source separation (BSS) has been progressing. BSS is a method to separate double source signals using only the signals observed by double sensors. In the pulse wave extraction method, various types of noises and PW are used as source signals, and the PW is extracted using BSS from the observed signal that is a mixture of these signals. However, since this method uses double sensors, there are still issues to be solved for practical use, such as adjusting the distance between sensors. In this study, we propose a single-input BSS method for PW extraction to solve this problem. In the proposed method, two series of signals are generated by a single input signal using QMF, and the BSS of these signals is used as the basis of the PW extraction method.
近年来,基于盲源分离(BSS)的脉冲波提取方法的研究取得了一定的进展。BSS是一种仅利用双传感器观测到的信号分离双源信号的方法。在脉冲波提取方法中,以各种类型的噪声和PW作为源信号,利用BSS从这些信号混合的观测信号中提取PW。然而,由于该方法使用了双传感器,因此在实际应用中还存在一些问题需要解决,例如传感器之间的距离调整。在本研究中,我们提出了一种单输入BSS方法用于PW提取来解决这一问题。在该方法中,利用QMF将单个输入信号生成两个序列信号,并将这些信号的BSS作为PW提取方法的基础。
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引用次数: 0
Beidou Carrier Phase Difference Reliability Improvement Method Applied to Medium and Large Fixed Wing UAV in Complex Environment 应用于复杂环境下大中型固定翼无人机的北斗载波相位差可靠性改进方法
Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931231
This paper aims at the problem of how to obtain the position information of self-height accuracy in real time when the medium and large UAV is affected by complex electromagnetic environment, high dynamics, large angle, high jitter and other factors in the process of task execution. Based on the Beidou carrier phase difference method, a method of integrity monitoring and a posteriori weight dynamic allocation is proposed to ensure the reliability of positioning results in complex environments. It is verified by simulation analysis and using the actual flight data of a certain UA V. The verification results show that the method introduced in this paper can make the UA V maintain high-precision and stable positioning in only 9 hours of mission.
本文针对中、大型无人机在执行任务过程中受到复杂电磁环境、高动态、大角度、高抖动等因素影响时,如何实时获取自高程精度的位置信息的问题。为了保证复杂环境下定位结果的可靠性,在北斗载波相位差方法的基础上,提出了一种完整性监测和后验权动态分配方法。通过仿真分析并利用某无人驾驶飞行器的实际飞行数据对该方法进行了验证。验证结果表明,本文所提出的方法可以使无人驾驶飞行器在9小时的任务时间内保持高精度、稳定的定位。
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引用次数: 0
Initial Implementation and Utilization of Instruction Dynamic Simulation in MEIMAT 教学动态仿真在MEIMAT中的初步实现与应用
Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931212
We have been developing MEIMAT, meiji micro-processor (MPU) architecture design tools. Our MEIMAT has a feature to implement arbitrary instructions. However, the MEIMAT does not have a function to simulate those instructions. Therefore, we have newly developed a function of simulation to dynamically execute the designed instructions. The result of this dynamic simulation can be measured by the developed module viewers. It is also demonstrated that a higher performance MPU can be designed by our improved MEIMAT.
我们一直在开发MEIMAT,明治微处理器(MPU)架构设计工具。我们的MEIMAT具有实现任意指令的特性。但是,MEIMAT没有模拟这些指令的功能。因此,我们新开发了仿真功能来动态执行所设计的指令。该动态仿真结果可以通过开发的模块查看器进行测量。实验还表明,改进后的MEIMAT可以设计出性能更高的微处理器。
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引用次数: 0
Speech Enhancement with Fractional Fourier Transform 分数阶傅里叶变换的语音增强
Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931218
The Fast Discrete Fourier Transform is commonly analysis method for speech signal processing in frequency domain. It has the problem of adjusting window size for desired resolution. But the Fractional Fourier Transform can have both time domain and frequency domain processing capabilities. This paper performs global processing by combining Fractional Fourier Transform and wavelet multi-scale local elimination on the speech signal and enhancement experiment with RNN neural network. The experimental results show that the speech signal is processed both locally by wavelet and globally by Fractional Fourier Transform. The combination of the two allows the speech signal to take into account both local and global information in the time and frequency domain.
快速离散傅里叶变换是频域语音信号处理的常用分析方法。它有调整窗口大小以满足所需分辨率的问题。但是分数阶傅里叶变换可以同时具有时域和频域处理能力。本文结合分数阶傅立叶变换和小波多尺度局部消去对语音信号进行全局处理,并用RNN神经网络进行增强实验。实验结果表明,小波变换对语音信号进行局部处理,分数阶傅里叶变换对语音信号进行全局处理。这两者的结合使得语音信号在时域和频域同时考虑到局部和全局信息。
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引用次数: 0
Task Offloading Strategies for Cloud-Side Cooperation in Compute-Intensive Scenarios Based on Edge Computing 计算密集型场景下基于边缘计算的云端协作任务卸载策略
Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931296
In computationally intensive scenarios, a large number of the terminal device is accessed locally. In order for the service to complete, the device needs to complete their tasks within the short time period set by the developers. Mobile Edge Computing (MEC) is an emerging technology deployed at the edge network of devices such as terminals and base stations, which can effectively meet the needs of computation-intensive scenarios. In this paper, we study a task offloading strategy based on edge computing to solve computationally intensive scenarios. We propose a collaborative cloud-edge task offloading model based on the framework of Mobile Device (MD), Edge Node (EN), and Cloud Computing (CC). Then we analyze the communication and computational requirements of the model and propose a linear offloading solution idea. We leverage this idea to propose a scheme to minimize the overall delay in the scene. The problem of the model is transformed into a non-convex problem by transforming it into a convex optimization thus solving the optimal offloading decision. Simulation results show that the scheme can effectively solve the delay optimization problem for computationally intensive scenarios.
在计算密集型场景下,大量终端设备需要本地访问。为了完成服务,设备需要在开发者设定的短时间内完成自己的任务。移动边缘计算(MEC)是一项部署在终端、基站等设备边缘网络的新兴技术,可以有效满足计算密集型场景的需求。在本文中,我们研究了一种基于边缘计算的任务卸载策略来解决计算密集型场景。提出了一种基于移动设备(MD)、边缘节点(EN)和云计算(CC)框架的协同云边缘任务卸载模型。然后分析了模型的通信和计算需求,提出了一种线性卸载的解决思路。我们利用这个想法提出了一个最小化场景整体延迟的方案。通过将模型问题转化为凸优化问题,求解最优卸载决策,从而将模型问题转化为非凸问题。仿真结果表明,该方案能够有效地解决计算密集型场景下的时延优化问题。
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引用次数: 0
A Multi-level Trusted Clustering Mechanism for IOT 物联网多层次可信集群机制
Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931247
To solve the problem of data transmission security in the perception layer network, we propose a multi-level trusted clustering mechanism. This mechanism combines multi-level clustering, identity authentication and trusted computing, we also propose a two-way transmission authentication method based on symmetric cryptography. Finally, platform integrity is authenticated. The authentication scheme improves the security of intra-cluster communication and enables the network to form a secured and trusted cluster structure.
为了解决感知层网络中数据传输的安全问题,提出了一种多级可信聚类机制。该机制结合了多级聚类、身份认证和可信计算,并提出了一种基于对称密码的双向传输认证方法。最后,对平台完整性进行验证。该认证方案提高了集群内部通信的安全性,使网络能够形成一个安全可信的集群结构。
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引用次数: 0
Fool Your Enemies: Enable Cyber Deception and Moving Target Defense for Intrusion Detection in SDN 欺骗你的敌人:启用网络欺骗和移动目标防御入侵检测在SDN
Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931208
The adoption of deception technology constructed to throw off stealthy attackers from real assets and gather intelligence about how they operate is gaining ground in the network system. Also, some static honeypots are deployed in the network system to attract adversaries for avoiding them accessing the real targets. This leads to a disclosure of the existence of cyber traps in the network that do not fool skillful attackers. Meanwhile, there are many intrusion detection systems (IDS) lack the abnormal traffic sample to obtain the knowledge of cyberattacks. Hence, it is vital to make honeypots more dynamically and give the material for harvesting useful threat intelligence for detector. Taking advantage of Software Defined Networking (SDN), cyber traps can be easily deployed when an intrusion detector triggers or actively laid in advance to mitigate the impact of adversaries into real assets. Instead of building IDS separately or blocking attacks promptly after an alert issued, in this paper, we utilize the strategy of associating Cyber Deception, and Moving Target Defense (MTD) with IDS in SDN, named FoolYE (Fool your enemies) to slow a network intruder down and leverage the behaviors of adversaries on traps for feeding back detector awareness.
在网络系统中,采用欺骗技术将隐秘的攻击者从真实资产中摆脱出来,并收集有关其运作方式的情报,这一技术正在取得进展。同时,在网络系统中部署一些静态蜜罐来吸引攻击者,避免攻击者访问真实目标。这将导致网络中存在无法欺骗熟练攻击者的网络陷阱的披露。同时,许多入侵检测系统缺乏异常流量样本来获取网络攻击的相关知识。因此,使蜜罐更加动态,为探测器获取有用的威胁情报提供材料至关重要。利用软件定义网络(SDN),当入侵探测器触发或主动提前设置时,可以轻松部署网络陷阱,以减轻攻击者对真实资产的影响。在本文中,我们没有单独构建IDS或在发出警报后立即阻止攻击,而是利用将网络欺骗和移动目标防御(MTD)与SDN中的IDS相关联的策略,称为愚(Fool your enemies),以减缓网络入侵者的速度,并利用陷阱上对手的行为来反馈探测器意识。
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引用次数: 1
Deep Learning-Based Variable Scaling Beam Training for Massive MIMO mmWave Systems 基于深度学习的大规模MIMO毫米波系统变尺度波束训练
Pub Date : 2022-09-27 DOI: 10.1109/ISCIT55906.2022.9931243
Improving the accuracy of beam training while reducing the training overhead and the influence of noise has become an important issue for massive multiple-input multiple-output (MIMO) millimeter-wave (mmWave) communication systems. In this paper, we propose a deep learning-based multi-scale neural network for beam training in massive MIMO mmWave communication system. The model predicts the orientation of narrow beams by learning the characteristics of wide beams, achieving high accuracy and low training overhead. Specifically, our model consists of three modules. In the first module, we deploy a convolutional neural network (CNN) to extract features of the instantaneous received signal of a wide beam. In the second module, we develop multi-scale convolution to extract wide beam features of the different time combination. In the third module, we conduct a long-term short-term memory (LSTM) network to calibrate the orientation of narrow beams based on previous predictions, thereby enhance the model's robustness to noise. Finally, according to the experimental results, our model improves beam training accuracy with low training overhead while reducing the influence of noise.
如何在提高波束训练精度的同时降低训练开销和噪声的影响,已成为大规模多输入多输出毫米波通信系统面临的重要问题。本文提出了一种基于深度学习的多尺度神经网络,用于大规模MIMO毫米波通信系统的波束训练。该模型通过学习宽波束的特征来预测窄波束的方向,实现了高精度和低训练开销。具体来说,我们的模型由三个模块组成。在第一个模块中,我们部署卷积神经网络(CNN)来提取宽波束的瞬时接收信号的特征。在第二个模块中,我们发展了多尺度卷积来提取不同时间组合的宽波束特征。在第三个模块中,我们进行了一个长期短期记忆(LSTM)网络,根据先前的预测校准窄波束的方向,从而增强模型对噪声的鲁棒性。实验结果表明,该模型在降低训练开销的同时提高了波束训练精度,降低了噪声的影响。
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引用次数: 0
期刊
2022 21st International Symposium on Communications and Information Technologies (ISCIT)
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