Iterative receivers for SFBC-OFDM systems with adaptive training scheme

This paper considers the design of iterative receivers for space-frequencyblock-coded orthogonal frequency division multiplexing (SFBC-OFDM) systems in unknown wirelessdispersive fading channels. An iterative joint channel estimation and symbol detection algorithm isderived. In the algorithm, the channel estimator performs alternately in two modes. During thetraining mode, the channel state information (CSI) is obtained by a discrete-Fourier-transform-basedchannel estimator and the noise variance and covariance matrix of the channel response is estimatedby the proposed method. In the data transmission mode, the CSI and transmitted data is obtainediteratively. In order to suppress the error propagation caused by a random error in identifyingsymbols, a simple error propagation detection criterion is proposed and an adaptive training schemeis applied to suppress the error propagation. Both theoretical analysis and simulation results showthat this algorithm gives better bit-error-rate performance and saves the overhead of OFDM systems.

Experimental Study of Discriminative Adaptive Training and MLLR for Automatic Pronunciation Evaluation

A stronger canonical model was developed to improve the performance of automatic pronunciation evaluations. Three different strategies were investigated with speaker adaptive training to normalize variations among speakers, minimum phone error training to identify easily confused phones and maximum likelihood linear regression （MLLR） adaptation to compensate for accent variations between native and non-native speakers. The three schemes were combined to improve the correlation coefficient between machine scores and human scores from 0.651 to 0.679 on the sentence level and from 0.788 to 0.822 on the speaker level.

A Global Training Model for Beat Classification Using Basic Electrocardiogram Morphological Features

Clinical Study and automatic diagnosis of electrocardiogram(ECG)data always remain a challenge in diagnosing cardiovascular activities.The analysis of ECG data relies on various factors like morphological features,classification techniques,methods or models used to diagnose and its performance improvement.Another crucial factor in themethodology is howto train the model for each patient.Existing approaches use standard training model which faces challenges when training data has variation due to individual patient characteristics resulting in a lower detection accuracy.This paper proposes an adaptive approach to identify performance improvement in building a training model that analyze global trainingmethodology against an individual training methodology and identifying a gap between them.We provide our investigation and comparative study on these methods and model with standard classification techniques with basic morphological features and Heart RateVariability(HRV)thatmay aid real time application.This approach helps in analyzing and evaluating the performance of different techniques and can suggests adoption of a best model identification with efficient technique and efficient attribute set for real-time systems.

Hybrid force control of astronaut rehabilitative training robot under active loading mode

In order to mitigate the effects of space adaptation syndrome(SAS) and improve the training efficiency of the astronauts, a novel astronaut rehabilitative training robot(ART) was proposed. ART can help the astronauts to carry out the bench press training in the microgravity environment. Firstly, a dynamic model of cable driven unit(CDU) was established whose accuracy was verified through the model identification. Secondly, to improve the accuracy and the speed of the active loading, an active loading hybrid force controller was proposed on the basis of the dynamic model of the CDU. Finally, the actual effect of the hybrid force controller was tested by simulations and experiments. The results suggest that the hybrid force controller can significantly improve the precision and the dynamic performance of the active loading with the maximum phase lag of the active loading being 9° and the maximum amplitude error being 2% at the frequency range of 10 Hz. The controller can meet the design requirements.

Potential benefits of Qi Gong meditation in quantifiable physiology: A five-year longitudinal observation

Objective: Qi Gong is a mind-and-body intervention to improve health and relieve disease conditions,which has been practiced for thousands of years. Lack of quantitative measures to monitor the quality and progress of Qi Gong practice has hindered the high-quality study to evaluate its health benefits.Methods: To develop such quantitative measures, vital sign changes in Qi Gong meditation practice of a single individual were monitored for 5 years by wearable devices.Results: This retrospective and exploratory analysis revealed significant changes in blood oxygen saturation, pulse rate, respiration rate, and perfusion index in response to Qi Gong practice and physiological adaptation to its long-term training. Among the physiological changes in vital signs, a physiological state with a significant 2-fold increase in pulse-respiration rate ratio was detected during Qi Gong meditation while the ratios were maintained at 4 under resting, sleeping, and mild cycling conditions.Conclusion: This is an inaugural report of a five-year longitudinal observation of this kind. Once validated in a well-designed cohort study, these non-invasive and easily obtained vital signs may be used as biomarkers to assess the adherence to breathing control during Qi Gong practice and can be applied to quantify the quality of Qi Gong practice in clinical trials with Qi Gong intervention.