An integrated microcomputer system for real-time monitoring of luminescent signals from multiple-well disks

An integrated microcomputer system interfacing an IBM PC XT/AT and compatible computers with an optical scanner is described. The system is capable of controlling the instruments (including a plotter used as the positioner for an optical biosensor) and obtaining and displaying the luminescent signals simultaneously as a function of time by different output units. The system is written mainly in Microsoft QuickBASIC 4.0 and it can also be easily modified to fulfil real-time monitoring of some other composition(s) or parameter(s).     

利用频谱变换的水声信号分类框架

在水声信号分类应用中,由于保密或采集条件限制等原因,样本通常会不足,导致深度学习框架的分类精度不高.为解决小样本水声信号分类精度不高的问题,提出一种结合频谱变换和深度学习框架的方法.通过对各类频谱变换测试,发现LOFAR频谱变换能显著提高声音信号中的特征表现.使用GAN网络对频谱变换后的样本扩充,使用改进的CNN网络对频谱图进行分类.实验结果表明了上述框架可以生成高质量的样本,显著提高水声信号的分类精度.     

An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism

Alcoholism affects the structure and functioning of brain. Electroencephalogram (EEG) signals can depict the state of brain. The EEG signals are ensemble of various neuronal activity recorded from different scalp regions having different characteristics and very low magnitude in microvolts. These factors make human interpretation difficult and time consuming to analyze these signals. Moreover, these highly varying EEG signals are susceptible to inter/intra variability errors. So, a Computer-Aided Diagnosis (CAD) can be used to identify the alcoholic and normal subjects accurately. However, these EEG signals exhibit nonlinear and non-stationary properties. Therefore, it needs much effort in deciphering the diagnostic evidence from them using linear time and frequency-domain methods. The nonlinear parameters together with time-frequency/scale domain methods can help to detect tiny changes in these signals. The correntropy is nonlinear indicator which characterizes the dynamic behavior of EEG signals in time-scale domain. In this paper, we present a new way for diagnosis of alcoholism using Tunable-Q Wavelet Transform (TQWT) based features derived from EEG signals. The feature extraction is performed using TQWT based decomposition and extracted Centered Correntropy (CC) from the forth decomposed detail sub-band. The Principal Component Analysis (PCA) is used for feature reduction followed by Least Squares-Support Vector Machine (LS-SVM) for classifying normal and alcoholic EEG signals. In order to make sure reliable classification performance, 10-fold cross-validation scheme is adopted. Our proposed system is able to diagnose the alcoholic and normal EEG signals, with an average accuracy of 97.02%, sensitivity of 96.53%, specificity of 97.50% and Matthews correlation coefficient of 0.9494 for Q-factor (Q) varying between 3 and 8 using Radial Basis Function (RBF) kernel function. Also, we have established a novel Alcoholism Risk Index (ARI) using three clinically significant features to discriminate the given classes by means of a single number. This system can be used for automated diagnosis and monitoring of alcoholic subjects to evaluate the effect of treatment.     

An improved OFDM chirp waveform used for MIMO SAR system

Special attention has been devoted to multi-input multi-output(MIMO)synthetic aperture radar(SAR)systems in recent years.The applications of MIMO SAR systems which involve 3D imaging,highresolution wide-swath remote sensing,and multi-baseline interferometry are seriously limited to the orthogonal waveforms.Although orthogonal frequency division multiplexing(OFDM)chirp waveforms can be used for MIMO SAR systems to avoid intra-pulse interferences,there is a small frequency shift between the transmitted OFDM pulses.This vulnerable shift,which can not only affect the waveform orthogonality,but also introduce residual phase error,renders the OFDM waveforms impractical.In this paper,an improved OFDM chirp waveform which works without the mentioned shift is presented,along with the novel modulation and efficient demodulation procedures.Comparison between the improved and the conventional OFDM chirp waveforms is detailed.The influence of random noise,quantization error,and Doppler shift on the orthogonality of OFDM waveform is also investigated in this paper.Theoretical analysis and simulation results illustrate the feasibility of this waveform scheme.     

An activity monitoring system for elderly care using generative and discriminative models

An activity monitoring system allows many applications to assist in care giving for elderly in their homes. In this paper we present a wireless sensor network for unintrusive observations in the home and show the potential of generative and discriminative models for recognizing activities from such observations. Through a large number of experiments using four real world datasets we show the effectiveness of the generative hidden Markov model and the discriminative conditional random fields in activity recognition.     

An efficient automated technique for CAD diagnosis using flexible analytic wavelet transform and entropy features extracted from HRV signals

Coronary Artery Disease (CAD) causes maximum death among all types of heart disorders. An early detection of CAD can save many human lives. Therefore, we have developed a new technique which is capable of detecting CAD using the Heart Rate Variability (HRV) signals. These HRV signals are decomposed to sub-band signals using Flexible Analytic Wavelet Transform (FAWT). Then, two nonlinear parameters namely; K-Nearest Neighbour (K-NN) entropy estimator and Fuzzy Entropy (FzEn) are extracted from the decomposed sub-band signals. Ranking methods namely Wilcoxon, entropy, Receiver Operating Characteristic (ROC) and Bhattacharya space algorithm are implemented to optimize the performance of the designed system. The proposed methodology has shown better performance using entropy ranking technique. The Least Squares-Support Vector Machine (LS-SVM) with Morlet wavelet and Radial Basis Function (RBF) kernels obtained the highest classification accuracy of 100% for the diagnosis of CAD. The developed novel algorithm can be used to design an expert system for the diagnosis of CAD automatically using Heart Rate (HR) signals. Our system can be used in hospitals, polyclinics and community screening to aid the cardiologists in their regular diagnosis.     

Classification of cardiac sound signals using constrained tunable-Q wavelet transform

The features extracted from the cardiac sound signals are commonly used for detection and identification of heart valve disorders. In this paper, we present a new method for classification of cardiac sound signals using constrained tunable-Q wavelet transform (TQWT). The proposed method begins with a constrained TQWT based segmentation of cardiac sound signals into heart beat cycles. The features obtained from heart beat cycles of separately reconstructed heart sounds and murmur can better represent the various types of cardiac sound signals than that from containing both. Therefore, heart sounds and murmur have been separated using constrained TQWT. Then the proposed novel raw feature set has been created by the parameters that have been optimized while constraining the output of TQWT together with that of extracted by using time-domain representation and Fourier–Bessel (FB) expansion of separated heart sounds and murmur. However, the adaptively selected features have been used to obtain the final feature set for subsequent classification of cardiac sound signals using least squares support vector machine (LS-SVM) with various kernel functions. The performance of the proposed method has been validated with publicly available datasets and the results have been compared with the existing short-time Fourier transform (STFT) based method. The proposed method shows higher percentage classification accuracy of 94.01 as compared to 93.53 of STFT based method. In comparison with STFT based method, it is noteworthy that the proposed method uses well defined and lower dimensionality of feature vector that can reduce the computational complexity.     

An adaptive predictive control system for random signals

A predictive system was described by Chestnut, et al. [4]. The purpose of this work is to increase the types of plants which can be controlled by such systems by making the fast time model adaptive, and to experimentally compare the random signal behavior of the resultant system with various other types of controllers. These controllers include a relay controller adjusted to switch when the error is zero, and a linear controller. According to the criteria of the minimum integral of the absolute error, the adaptive predictive controller delivered the most satisfactory-responses for second- and third-order systems.     

广播电视信号的自动监测系统设计

提出一种广播电视信号的自动监测系统,以改变传统的人工定期外出收测方式。在广播电视覆盖区范围内的多个监测点上设置前端设备,用于接收广播电视信号并将信号频率、场强、监测点名称等信息通过网络传输到处理终端,进行监测点信号场强的自动检测和分析。前端设备以单片机为核心,连接天线和选频电路、检波电路、模数转换器、数模转换器以及网络收发器,通过软件循环选频、处理场强数据。网络收发器实现前端设备以有线或无线方式接入互联网,从而与处理终端连接和数据交互。处理终端以桌上型PC、膝上型PC或者掌上PC为平台,通过软件远程接收互联网传输的的数据,对每个监测点上的前端设备所发送的信号频率、场强、监测点名称等信息进行存储和显示。系统运行正常,数据精确,方便快捷,稳定可靠,省时省力,具有很高的推广价值。     

An enhanced diagnostic system for gear system monitoring.

The detection of the onset of damage in gear systems (e.g., gearboxes)is of great importance to a wide array of industries. In this paper, an enhanced diagnostic (ED) system is developed for real-time gear system condition monitoring. A neurofuzzy (NF) paradigm is adopted for pattern classification of the features from the energy, amplitude, and phase domains. The diagnostic reliability is enhanced by properly integrating predicted future machinery states that are forecast by recurrent NF predictors. An online training technique is proposed to improve the classifier's adaptive capability to accommodate different machinery conditions. The viability of this new monitoring system has been verified by experimental tests under different gear conditions. This proposed ED system has also been applied for real-time condition monitoring in multistage printing machines. The primary application has demonstrated its reliability as an effective monitoring tool for both production quality control and maintenance planning.     

A hybrid system for SPC concurrent pattern recognition

Any nonrandom patterns shown in Statistical Process Control (SPC) charts imply possible assignable causes that may deteriorate the process performance. Hence, timely detecting and recognizing Control Chart Patterns (CCPs) for nonrandomness is very important in the implementation of SPC. Due to the limitations of run-rule-based approaches, Artificial Neural Networks (ANNs) have been resorted for detecting CCPs. However, most of the reported ANN approaches are only limited to recognize single basic patterns. Different from these approaches, this paper presents a hybrid approach by integrating wavelet method with ANNs for on-line recognition of CCPs including concurrent patterns. The main advantage of this approach is its capability of recognizing coexisted or concurrent patterns without training by concurrent patterns. The test results using simulated data have demonstrated the improvements and the effectiveness of the methodology with a success rate up to 91.41% in concurrent CCP recognition.     

Automatic feature extraction of waveform signals for in-process diagnostic performance improvement

In this paper, a new methodology is presented for developing a diagnostic system using waveform signals with limited or with no prior fault information. The key issues studied in this paper are automatic fault detection, optimal feature extraction, optimal feature subset selection, and diagnostic performance assessment. By using this methodology, a diagnostic system can be developed and its performance is continuously improved as the knowledge of process faults is automatically accumulated during production. As a real example, the tonnage signal analysis for stamping process monitoring is provided to demonstrate the implementation of this methodology.     

An immune-theory-based model for monitoring inter-domain routing system

The inter-domain routing system faces many serious security threats because the border gateway protocol(BGP) lacks effective security mechanisms.However,there is no solution that satisfies the requirements of a real environment.To address this problem,we propose a new model based on immune theory to monitor the inter-domain routing system.We introduce the dynamic evolution models for the "self" and detection cells,and construct washout and update mechanisms for the memory detection cells.Furthermore,borrowing an idea from immune network theory,we present a new coordinative method to identify anomalous nodes in the inter-domain routing system.In this way,the more nodes working with their own information that join the coordinative network,the greater is the ability of the system to identify anomalous nodes through evaluation between nodes.Because it is not necessary to modify the BGP,the ITMM is easy to deploy and inexpensive to implement.The experimental results confirm the method’s ability to detect abnormal routes and identify anomalous nodes in the inter-domain routing system.     

一种改进的域名服务器监控系统

传统的域名服务器监控系统通过分析服务器的日志以达到监控服务器的目的.这种监控系统难以达到实时监控的目的.本文提出并实现了一种改进的监控系统,该系统通过自主地捕获域名请求响应数据包,能够实时地监控域名服务器,并且由于关闭了域名服务器的日志功能,提高了域名服务器的性能.     

An advanced video-based system for monitoring active volcanoes

Video surveillance systems are consolidated techniques for monitoring eruptive phenomena in volcanic areas. Along with these systems, which use standard video cameras, people working in this field sometimes make use of infrared cameras providing useful information about the thermal evolution of eruptions. Real-time analysis of the acquired frames is required, along with image storing, to analyze and classify the activity of volcanoes. Human effort and large storing capabilities are hence required to perform monitoring tasks.In this paper we present a new strategy aimed at improving the performance of video surveillance systems in terms of human-independent image processing and storing optimization. The proposed methodology is based on real-time thermo-graphic analysis of the area considered. The analysis is performed by processing images acquired with an IR camera and extracting information about meaningful volcanic events.Two software tools were developed. The first provides information about the activity being monitored and automatically adapts the image storing rate. The second tool automatically produces useful information about the eruptive activity encompassed by a selected frame sequence.The software developed includes a suitable user interface allowing for convenient management of the acquired images and easy access to information about the volcanic activity monitored.     

An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network

This paper describes a fault diagnosis system for automotive generators using discrete wavelet transform (DWT) and an artificial neural network. Conventional fault indications of automotive generators generally use an indicator to inform the driver when the charging system is malfunction. But this charge indicator tells only if the generator is normal or in a fault condition. In the present study, an automotive generator fault diagnosis system is developed and proposed for fault classification of different fault conditions. The proposed system consists of feature extraction using discrete wavelet analysis to reduce complexity of the feature vectors together with classification using the artificial neural network technique. In the output signal classification, both the back-propagation neural network (BPNN) and generalized regression neural network (GRNN) are used to classify and compare the synthetic fault types in an experimental engine platform. The experimental results indicate that the proposed fault diagnosis is effective and can be used for automotive generators of various engine operating conditions.     

Frequency domain system identification using arbitrary signals

It is the common conviction that frequency domain system identification suffers from the drawback that it cannot handle arbitrary signals without introducing systematic errors. This paper shows that it is possible to deal with nonperiodic signals without any approximation and under the same assumptions as in the time domain, by estimating simultaneously some initial conditions and the system model parameters