基于矩阵联合分块对角化的通信信号监测

在非协作通信和军事通信对抗中,接收机需要对接收到的多个通信信号进行分离,以提取出有用的信号,这可以归为通信信号卷积盲分离问题。首先构建信号模型,并将问题转化为多个矩阵的联合分块对角化。然后提出一种新的非正交矩阵联合分块对角化算法,使用最速下降法得到迭代算法,并分析了算法的可能优化策略和计算复杂度。最后仿真实验验证了新算法的有效性和可靠性,在无须计算最优步长等条件下能够获得比现有算法更快的收敛速度。     

欠定条件下弱稀疏源信号混合矩阵盲估计

针对源信号的稀疏性影响欠定混合矩阵的估计精度, 在源信号单源频率及非单源频率分量分析的基础上,通过对观测信号频率峰值的幅值比值所 构成的列向量聚类,提出欠定条件下弱稀疏源信号混合矩阵的盲估计方法。鉴于经典聚类算 法的局部收敛性带来聚类结果的不稳定性,采用全局收敛特性较好的遗传模拟退火聚类算法 提高聚类结果的鲁棒性。仿真实验表明,本文提出的混合矩阵估计方法及采用的聚类算法 在不同欠定条件及噪声环境下具有较强的估计性能。     

基于相位编码的音频信息隐藏方法

针对听觉感知无差异时的局部相位失真条件,在宽带音频信号实验的基础上,提出一种利用局部相位分区实现信息隐藏的新算法。相对于经典的相位调制算法,利用局部相位信息,在保证听觉相似性的同时,能提高信息的嵌入量、改善误码率。理论分析和实验结果表明了该算法的有效性。     

Design of Online Vitals Monitor by Integrating Big Data and IoT

In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis. We use Apache Kafka (to gather live data streams from connected sensors), Apache Spark (to categorize the patient vitals and notify the medical professionals while identifying abnormalities in physiological parameters), Hadoop Distributed File System (HDFS) (to archive data streams for further analysis and long-term storage), Spark SQL, Hive and Matplotlib (to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals). In addition, we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely. Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing, data processing, and data transmission mechanisms. To validate the system accuracy, we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor, the Welch Allyn® Spot Check. Our proposed system provides improved care solutions, especially for those whose access to care services is limited.     

基于STM32和小波自适应滤波算法的生理参数监测系统的研究

针对心血管疾病的高死亡率以及人口老龄化的现象,本篇文章开发了基于STM32单片机和小波自适应阈值滤波算法的可穿戴式健康监测系统。系统可分为系统微处理器、数字系统模块、人机交互模块、信号采集模块和无线通信模块等几个部分,针对人体的心率、血氧、体温等重要生理参数进行处理分析,进而对人体实时监护。系统处理器选取STM32F103C8T6作为控制芯片,显示模块选用了OLED。生理参数采集系统选用了MAX30102传感器和Pulse sense传感器分别对人体腕部和指尖心率进行采集。生理参数采集完毕后,通过进一步的A/D转化,基于提出的一种改进小波自适应阈值滤波算法降噪滤波,从而将人体的生理特征参数记录下来。再将采集的生理数据通过蓝牙传输至手机端,其中的ZigBee模块主要是把获得的数据再次输送到远程控制端内,让患者能够远程得到更好的医疗监控。本系统通过软件与硬件相结合的方式。最后通过对比论证其中心率（BPM）结果误差为±2BPM,血氧含量监测结果误差在±2%以内。     

“信号与系统”课程教学实例

信号频谱分析是“信号与系统”课程中的重点内容,但学生在理解相关概念和理论时常存在较大的困难。针对这种现象,本文介绍了一种信号频谱分析的教学实例。该实例利用傅里叶变换的数值计算方法,对太阳黑子数进行频谱分析,并确定太阳活动的周期。本教学实例注重引导学生理论联系实际,既深化学生对频谱分析的基本概念与基本理论的理解,又有利于培养学生分析解决实际问题的能力。     

RFID Positioning and Physiological Signals for Remote Medical Care

The safety of patients and the quality of medical care provided to them are vital for their wellbeing. This study establishes a set of RFID (Radio Frequency Identification)-based systems of patient care based on physiological signals in the pursuit of a remote medical care system. The RFID-based positioning system allows medical staff to continuously observe the patient's health and location. The staff can thus respond to medical emergencies in time and appropriately care for the patient. When the COVID-19 pandemic broke out, the proposed system was used to provide timely information on the location and body temperature of patients who had been screened for the disease. The results of experiments and comparative analyses show that the proposed system is superior to competing systems in use. The use of remote monitoring technology makes user interface easier to provide high-quality medical services to remote areas with sparse populations, and enables better care of the elderly and patients with mobility issues. It can be found from the experiments of this research that the accuracy of the position sensor and the ability of package delivery are the best among the other related studies. The presentation of the graphical interface is also the most cordial among human-computer interaction and the operation is simple and clear.     

Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection

The electroencephalogram (EEG) is the frequently used signal to detect epileptic seizures in the brain. For a successful epilepsy surgery, it is very essential to localize epileptogenic area in the brain. The signals from the epileptogenic area are focal signals and signals from other area of the brain region nonfocal signals. Hence, the classification of focal and nonfocal signals is important for locating the epileptogenic area for epilepsy surgery. In this article, we present a computer aided automatic detection and classification method for focal and nonfocal EEG signal. The EEG signal is decomposed by Dual Tree Complex Wavelet Transform (DT‐CWT) and the features are computed from the decomposed coefficients. These features are trained and classified using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The proposed system achieves 98% sensitivity, 100% specificity, and 99% accuracy for EEG signal classification. The experimental results are presented to show the effectiveness of the proposed classification method to classify the focal and nonfocal EEG signals. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 277–283, 2016