一种低成本光接收器的数据恢复电路的设计及FPGA实现

设计了一种利用FPGA的可编程输入延时单元(IDELAY)和锁相环输出同频多相时钟结合的4倍过采样高速时钟数据恢复电路。可在较低频率同步恢复4位并行数据,有效地增大带宽并降低了终端成本,并采用自动检测和判断的方法检测数据跳变边沿,消除了数据毛刺的干扰。     

基于过采样技术的生物电信号检测

 为充分发挥中等分辨率模数转换器的速度潜力和简化生物电放大器的设计、大幅度降低放大器的成本,本文采用过采样技术对生物电信号进行检测.以人体心电检测为例,使用三种采样方法进行实验.结果表明,过采样得到的心电图形有较好的精度和信噪比.该方法可广泛应用于各种生物电信号的检测.     

整合DBSCAN和改进SMOTE的过采样算法

针对SMOTE(Synthetic Minority Over-sampling Technique)等传统过采样算法存在的忽略类内不平衡、扩展少数类的分类区域以及合成的新样本高度相似等问题,基于综合考虑类内不平衡和合成样本多样性的思想,提出了一种整合DBSCAN和改进SMOTE的过采样算法DB-MCSMOTE(DBSCAN and Midpoint Centroid Synthetic Minority Over-sampling Technique)。该算法对少数类样本进行DBSCAN聚类,根据提出的簇密度分布函数,计算各个簇的簇密度和采样权重,在各个簇中利用改进的SMOTE算法(MCSMOTE)在相距较远的少数类样本点之间的连线上进行过采样,提高合成样本的多样性,得到新的类间和类内综合平衡数据集。通过对一个二维合成数据集和九个UCI数据集的实验表明,DB-MCSMOTE可以有效提高分类器对少数类样本和整体数据集的分类性能。     

基于过采样的归一化累积量盲均衡算法

传统的波特间隔均衡器只能补偿接收信号混叠的频率响应特性,并不能补偿信道畸变。针对这一问题提出一种基于过采样的归一化累积量算法,先对接收信号进行过采样,再利用变步长自适应调整均衡器的抽头系数,不仅能避免算法陷入局部最优,而且还能有效补偿信道畸变。仿真表明:引入过采样和变步长的盲均衡算法,有效地提高了收敛速度,降低了稳态剩余误差。     

面向非平衡类问题的k近邻分类算法

针对k近邻（kNN）方法不能很好地解决非平衡类问题,提出一种新的面向非平衡类问题的k近邻分类算法。与传统k近邻方法不同,在学习阶段,该算法首先使用划分算法（如K-Means）将多数类数据集划分为多个簇,然后将每个簇与少数类数据集合并成一个新的训练集用于训练一个k近邻模型,即该算法构建了一个包含多个k近邻模型的分类器库。在预测阶段,使用划分算法（如K-Means）从分类器库中选择一个模型用于预测样本类别。通过这种方法,提出的算法有效地保证了k近邻模型既能有效发现数据局部特征,又能充分考虑数据的非平衡性对分类器性能的影响。另外,该算法也有效地提升了k近邻的预测效率。为了进一步提高该算法的性能,将合成少数类过抽样技术（SMOTE）应用到该算法中。KEEL数据集上的实验结果表明,即使对采用随机划分策略划分的多数类数据集,所提算法也能有效地提高k近邻方法在评价指标recall、g-mean、f-measure和AUC上的泛化性能;另外,过抽样技术能进一步提高该算法在非平衡类问题上的性能,并明显优于其他高级非平衡类处理方法。     

短波射频数字化接收机的设计与实现

针对传统中频数字化接收机复杂度高、灵活性差等问题,设计并实现了一种新型短波射频数字化接收机.该接收机由A/D、数字下变频器(DDC)、DSP、D/A、CPLD等几部分组成,实现了短波信号的射频数字化处理.详细介绍了接收机的设计方案,并对常见的调幅(AM)、等幅报(CW)、单边带(ISB)等信号进行了接收测试.在输入电平为-103～0 dB动态范围内,解调输出质量都能达到12 dB左右信纳得(SND).     

Oversampling Method Based on Gaussian Distribution and K-Means Clustering

Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years. When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority. Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications. Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this paper, we propose a simple oversampling method based on Multivariate Gaussian distribution and K-means clustering, called GK-Means. The new method aims to avoid generating noise and control imbalances between and within classes. Various experiments have been carried out with six classifiers and four oversampling methods. Experimental results on different imbalanced datasets show that the proposed GK-Means outperforms other oversampling methods and improves classification performance as measured by F1-score and Accuracy.     

ΣΔA/D转换及其在CMOS图像传感器中的应用

介绍了过采样ΣΔA/D转换器的原理,分析了这种转换器精度高、结构简单、易于实现的优点以及符合CMOS工艺集成要求的特点。根据这些特点指出,过采样ΣΔA/D更适合于CMOS图像传感系统并易于制造片上系统。最后,指出了过采样ΣΔA/D转换器在CMOS图像传感领域的应用特点。     

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed. Hence, this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis. This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment. This can be particularly helpful when dealing with smaller datasets. Furthermore, our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms. This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts. The experimental results validate that our model offers excellent results with a validation accuracy of 92.5% and an F1 measure of 0.92.     

Low-clock-speed time-interleaved architecture for a polar delta–sigma modulator transmitter

The polar delta–sigma modulator (DSM) transmitter architecture exhibits good coding efficiency and can be used for software-defined radio applications. However, the necessity of high clock speed is one of the major drawbacks of using this transmitter architecture. This study proposes a low-complexity time-interleaved architecture for the polar DSM transmitter baseband part to reduce the clock speed requirement of the polar DSM transmitter using an upsampling technique. Simulations show that using the proposed four-branch time-interleaved polar DSM transmitter baseband part, the clock speed requirement of the transmitter is reduced by four times without degrading the signal-to-noise-and-distortion ratio.