Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information

Several real-time short-term prediction methods, based on time-series modeling of past continuous glucose monitoring (CGM) sensor data have been proposed with the aim of allowing the patient, on the basis of predicted glucose concentration, to anticipate therapeutic decisions and improve therapy of type 1 diabetes. In this field, neural network (NN) approaches could improve prediction performance handling in their inputs additional information. In this contribution we propose a jump NN prediction algorithm (horizon 30 min) that exploits not only past CGM data but also ingested carbohydrates information. The NN is tuned on data of 10 type 1 diabetics and then assessed on 10 different subjects. Results show that predictions of glucose concentration are accurate and comparable to those obtained by a recently proposed NN approach (Zecchin et al. (2012) [26]) having higher structural and algorithmical complexity and requiring the patient to announce the meals. This strengthen the potential practical usefulness of the new jump NN approach.     

Turkish Cypriot Mufti Struggle in the Media

The aim of this article is to provide information on the struggle to re-establish the Mufti of Cyprus, （abolished in 1928 by the British colonial government） in the context of the power of Turkish society media which began to be emphasized and highlighted in the pages of ＂Halkin Sesi＂ （People＇s Voice） and ＂lstiklal＂ （Independence） newspapers first with articles to emphasize the importance of Muftis Struggle. Communication, which is one of the most essential properties in a society, is the key to inculcate the idea of social solidarity into people. The most important media of the period was the print press and this study examines its contributions to social solidarity of the community during British Period in Cyprus. During this period, the two most influential news paper ＂Halkln Sesi＂ and ＂lstiklal＂ and their communicational skills had countless effects on community and social solidarity. In this study, these effects and influences over a specific struggle are examined and interpreted. With this study, it is concluded that these two newspapers had very big impacts on the media of that period and they had really important contributions on the Mufti struggle of the community.     

一种基于改进K均值算法的跳频信号分选方法

为了在估计聚类数目的同时避免收敛到局部极小值,提出了一种改进的初始聚类中心选取算法。该算法通过形态学处理搜索统计直方图的峰值,根据合并门限正确估计聚类中心的数目和初始位置。将其与传统的K均值算法相结合,可用于跳频信号分选。实验结果表明,与其他K均值算法相比,该改进K均值算法能够以很高的正确率分选跳频信号。     

Tektronix混合信号存储示波器在信号处理中的应用研究

介绍矩形窗函数的实验及TektrolqiXMS02000型混合信号永波器为数据处理和分析提供了大量真实的原始实验数据,及与仿真软件结合进行信号处理,克服信号采集和数字化硬件的瓶颈制约。     

Posix同步机制在UNIX实时应用软件中的实现

在UNIX多进程、多线程实时应用中,同步机制是保证共享数据同步和完整的重要手段.通过一个应用实例,介绍了Posix互斥锁、条件变量、读写锁等同步机制和信号技术在多进程、多线程实时应用软件中的实现方法.     

基于Xhtml+Voice英语语音训练系统的开发应用

Xhtml+Voice结合了Xhtml和VoiceXML各自的特点,把VoiceXML的语音交互移植到Web中,使开发带有语音交互的Web应用更灵活、更高效.提出了Xhtml+Voice在英语语音训练系统的应用实例,这种基于Xhtml+Voice的应用,增加了英语语音训练的灵活性和交互性.     

Autoregressive modeling of speech trajectory transformed to the reconstructed phase space for ASR purposes

Investigating new effective feature extraction methods applied to the speech signal is an important approach to improve the performance of automatic speech recognition (ASR) systems. Owing to the fact that the reconstructed phase space (RPS) is a proper field for true detection of signal dynamics, in this paper we propose a new method for feature extraction from the trajectory of the speech signal in the RPS. This method is based upon modeling the speech trajectory using the multivariate autoregressive (MVAR) method. Moreover, in the following, we benefit from linear discriminant analysis (LDA) for dimension reduction. The LDA technique is utilized to simultaneously decorrelate and reduce the dimension of the final feature set. Experimental results show that the MVAR of order 6 is appropriate for modeling the trajectory of speech signals in the RPS. In this study recognition experiments are conducted with an HMM-based continuous speech recognition system and a naive Bayes isolated phoneme classifier on the Persian FARSDAT and American English TIMIT corpora to compare the proposed features to some older RPS-based and traditional spectral-based MFCC features.     

Support vector regression for anomaly detection from measurement histories

This research focuses on the analysis of measurements from distributed sensing of structures. The premise is that ambient temperature variations, and hence the temperature distribution across the structure, have a strong correlation with structural response and that this relationship could be exploited for anomaly detection. Specifically, this research first investigates whether support vector regression (SVR) models could be trained to capture the relationship between distributed temperature and response measurements and subsequently, if these models could be employed in an approach for anomaly detection. The study develops a methodology to generate SVR models that predict the thermal response of bridges from distributed temperature measurements, and evaluates its performance on measurement histories simulated using numerical models of a bridge girder. The potential use of these SVR models for damage detection is then studied by comparing their strain predictions with measurements collected from simulations of the bridge girder in damaged condition. Results show that SVR models that predict structural response from distributed temperature measurements could form the basis for a reliable anomaly detection methodology.     

Evaluating perceived voice quality on packet networks using different random neural network architectures

Voice over Internet Protocol (VoIP) is one of the fastest growing technologies in the world. In VoIP speech signals are transmitted over the same network used for data communications. The internet is not a robust network and is subjected to delay, jitter, and packet loss. It is very important to measure and monitor the quality of service (QoS) the users experience in VoIP networks; this is not an easy task and usually requires subjective tests. In this paper we have analyzed three non-intrusive models to measure and monitor voice quality using Random Neural Networks (RNN). A RNN is an open queuing network with positive and negative signals. We have assessed the voice quality based on various parameters i.e. delay, jitter, packet loss, and codec. In our approach we have used the Mean Opinion Score (MOS) calculated using a Perceptual Evaluation of Speech Quality (PESQ) algorithm to generate data for training the RNN model. We have studied two feed-forward models and a recurrent architecture. We have found that the simple feed-forward architecture has produced the most accurate results compared to the other two architectures.     

An improved noise-robust voice activity detector based on hidden semi-Markov models

To improve the performance of voice activity detector (VAD) in noisy environments, this paper concentrates on three critical aspects related to noise robustness including speech features, feature distributions and temporal dependence. Based on the statistic on TIMIT and NOIZEUS, Mel-frequency cepstrum coefficients (MFCCs) are selected as speech features, Gaussian Mixture distributions (GMD) are applied to associate the observations in MFCC domain with both speech and non-speech states, and Weibull and Gamma distributions are used to explicitly model noise and speech durations, respectively. To integrate these aspects into VAD, the hidden semi-Markov model (HSMM) as a generalized hidden Markov model (HMM) is introduced first. Then the VAD decision is made according to the likelihood ratio test (LRT) incorporating state prior knowledge and modified forward variables of HSMM. We design a recursive way to efficiently calculate modified forward variables. Finally a series of experiments demonstrate: (1) the positive effect of different robustness-related schemes adopted in the proposed VAD; (2) better performance against the standard ITU-T G.729B, Adaptive MultiRate VAD phase 2 (AMR2), Advanced Front-end (AFE), HMM-based VAD and VAD using Laplacian-Gaussian model (LD-GD based VAD).