共查询到11条相似文献，搜索用时 46 毫秒
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在语种识别过程中,为提取语音信号中的空间特 征以及时序特征,从而达到提高多语 种识别准确率的目的,提出了一种利用卷积循环神经网络(convolutional recurrent neural network,CRNN)混合神经网络的多语种识别模型。该模型首先提 取语音信号的声学特征;然后将特征输入到卷积神经网络(convolutional neural network,CNN) 提取低维度的空间特征;再通过空 间金字塔池化层(spatial pyramid pooling layer,SPP layer) 对空间特征进行规整,得到固定长度的一维特征;最后将其输入到循环神经 网络(recurrenrt neural network,CNN) 来判别语种信息。为验证模型的鲁棒性,实验分别在3个数据集上进行,结果表明:相 比于传统的CNN和RNN,CRNN混合神经网络对不同数据集的语种识别 准确率均有提高,其中在8语种数据集中时长为5 s的语音上最为明显,分别提高了 5.3% 和6.1%。 相似文献
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Aditya Sai Srinivas T. Ramasubbareddy Somula Govinda K. Akriti Saxena Pramod Reddy A. 《International Journal of Communication Systems》2020,33(13)
The precision of forecasting rainfall is vital owing to current world climate change. As deterministic weather forecasting models are usually time consuming, it becomes challenging to efficiently use this large volume of data in hand. Machine learning methods are already proven to be good replacement for traditional deterministic approaches in weather prediction. This paper presents an approach using recurrent neural networks (RNN) and long short term memory (LSTM) techniques to improve the rainfall forecast performance. This will be compared with the random forest classifier and XGBoost as well. The goal is to predict a set of hourly rainfall levels from sequences of weather radar measurements. Python libraries are utilized to forecast the time series data. The training set comprises of data from first 20 days of every month and the inference set data from the continuing days. This makes sure that both train and inference sets are more or less independent. The idea resides in implementing an end‐to‐end learning framework. 相似文献
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Anuradha Banerjee Sachin Kumar Gupta Parul Gupta Abu Sufian Ashutosh Srivastava Manoj Kumar 《International Journal of Communication Systems》2023,36(14):e5555
UAVs are capable of providing significant potential to IoT devices through sensors, cameras, GPS systems, and so forth. Therefore, the smart UAV-IoT collaborative system has become a current hot research topic. However, other concerns require in-depth investigation and study, such as resource allocation, security, privacy preservation, trajectory optimization, intelligent decision-making, energy harvesting, and so forth. Here, we suggest a task-scheduling method that splits IoT devices into distinct clusters based on physical proximity and saves time and energy. Cluster heads can apply an auto regressive moving average (ARMA) model to predict intelligently the timestamp of the arrival of the next task and associated estimated payments. Based on the overall expected payment, a cluster head can smartly advise the UAV about its time of next arrival. According to the findings of the simulation, the proposed ETTS algorithm significantly outperforms Task TSIE and TDMA-WS in terms of energy use (67%) and delays (36%). 相似文献
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在上行非正交多址(Non-orthogonal Multiple Access, NOMA)系统中,针对传统基于串行干扰消除(Successive Interference Cancellation, SIC)检测存在同个时频块内用户间干扰的问题,提出了一种新型的NOMA检测算法。通过将SIC检测的反馈消除结构和深度神经网络结合起来,设计出了一种新型的反馈深度神经网络(Feedback Deep Neural Network, FDNN)结构。FDNN模型分为两个模块,检测模块通过深度神经网络实现非线性检测,反馈模块通过权重矩阵重构信号并消除用户干扰。通过重复检测和反馈过程,FDNN依次检测出各个用户的符号,并达到了良好的性能。仿真结果表明FDNN检测算法相较于SIC检测具有更低的误符号率和误比特率,并验证了其具有更良好的抗用户间干扰的性能。 相似文献
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Zhiting Lin Zhongzhen Tong Jin Zhang Fangming Wang Tian Xu Yue Zhao Xiulong Wu Chunyu Peng Wenjuan Lu Qiang Zhao Junning Chen 《半导体学报》2022,43(3):031401-031401-25
Artificial intelligence (AI) processes data-centric applications with minimal effort. However, it poses new challenges to system design in terms of computational speed and energy efficiency. The traditional von Neumann architecture cannot meet the requirements of heavily data-centric applications due to the separation of computation and storage. The emergence of computing in-memory (CIM) is significant in circumventing the von Neumann bottleneck. A commercialized memory architecture, static random-access memory (SRAM), is fast and robust, consumes less power, and is compatible with state-of-the-art technology. This study investigates the research progress of SRAM-based CIM technology in three levels: circuit, function, and application. It also outlines the problems, challenges, and prospects of SRAM-based CIM macros. 相似文献
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Rajdeep Singh Sohal Vinit Grewal Kuldeep Singh Jaipreet Kaur 《International Journal of Communication Systems》2023,36(2):e5377
The omnipresence of drones in the civilian air space has led to their malicious usage raising high alert security issues. In this paper, a deep learning approach to detect and identify drones and to determine their flight modes from the remotely sensed radio frequency (RF) signatures is presented. This work intends to detect the presence of drones using two-class classification, the presence along with identification of their make using four-class classification. And this is further extended to the determination of their flight modes using ten-class classification. It employs the proposed architectures of prominent deep learning classifiers, namely, autoencoder (AE), long short-term memory (LSTM), convolutional neural network (CNN), and CNN-LSTM hybrid model. To procure the relevant information from 227 RF signatures having 100 fragments each, the seven significant temporal statistical features, namely, maxima, minima, mean, variance, skewness, kurtosis, and root mean square, are extracted. In a two-class classification scenario, all considered classifiers perform near to idle, whereas in a four-class classification scenario, CNN performs best, followed by AE, CNN-LSTM, and LSTM, respectively. Moreover, in a ten-class classification scenario, AE far outperforms CNN, followed by LSTM and CNN-LSTM, respectively. The best performance in terms of accuracy and classification time confirms the feasibility of the proposed AE classifier for the three considered drone operations. 相似文献
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卷积神经网络(convolutional neural network, CNN)作为传统神经网络的改进,已经得到了广泛的应用。然而,在CNN性能提升的同时其模型的规模不断扩大,对存储及算力的要求越来越高,基于冯·诺依曼体系结构的处理器难以达到令人满意的高处理性能。为了提升系统性能,近存储计算(near memory computing, NMC)成为了一个具有发展前景的研究方向。本文利用一种支持NMC的可重构阵列处理器实现手写数字识别,并行地实现了卷积运算;同时利用共享缓存阵列结构,减少片外存储的频繁访问。实验结果表明,在110 MHz的工作频率下,执行单个5×5卷积运算的计算速度提升了75.00%,可以在9 960μs内实现一个手写数字的识别。 相似文献