Finite-region boundedness and stabilization for 2D continuous-discrete systems in Roesser model

This paper investigates the finite-region boundedness (FRB) and stabilization problems for two-dimensional continuous-discrete linear Roesser models subject to two kinds of disturbances. For two-dimensional continuous-discrete system, we first put forward the concepts of finite-region stability and FRB. Then, by establishing special recursive formulas, sufficient conditions of FRB for two-dimensional continuous-discrete systems with two kinds of disturbances are formulated. Furthermore, we analyze the finite-region stabilization issues for the corresponding two-dimensional continuous-discrete systems and give generic sufficient conditions and sufficient conditions that can be verified by linear matrix inequalities for designing the state feedback controllers which ensure the closed-loop systems FRB. Finally, viable experimental results are demonstrated by illustrative examples

Efferent Modulation of Spontaneous Activity in Developing Sensory Systems

Patterned spontaneous activity plays an instructive role in developing sensory systems. Before hearing onset, inner support cells release ATP and induce spontaneous firing of neighboring inner hair cells. This periphery-initiated spontaneous activity propagates throughout the auditory hierarchy via the afferent pathway, coordinating neural activity in distinct tonotopic zones in the central auditory system. Similarly, spontaneous retinal waves initiated in the retina by starburst amacrine cells (stage II) or bipolar cells (stage III) were observed throughout the visual system via the retinotopic visual afferent circuits. Deciphering the underlying mechanisms of patterned spontaneous activity is critical to elucidate its instructive role in priming the developing nervous system prior to sensory experience. On the other hand, anatomical and functional evidence suggests that centrifugal efferent systems may contribute to neural dynamics before sensory inputs. In the first half of this study, we profiled spatiotemporal and correlational features of auditory spontaneous activity over the entire pre-hearing period. We discovered that the olivocochlear efferent system controlled the coupling strength of bilateral auditory spontaneous activity and demonstrated the profound impact of such modulation on the development of auditory functions. In the second half of this work, we introduced a novel experimental technique that enabled access to in situ retinal calcium dynamics in awake animals. We demonstrated in situ recordings of spontaneous retinal waves from distinct neuronal populations in the retina. Moreover, our result indicated that retinal activity was directly modulated by locomotion. Our approach is well suited to study retinopetal projections in vivo and whether they contributed to locomotion-related modulation on retinal dynamics. Together, these findings provide new perspectives on the functional roles of efferent modulations in shaping spontaneous activity and promoting the development of auditory and visual systems

A Portable Impedance Biosensing System based on a Laptop with LabVIEW for Rapid Detection of Avian Influenza Virus

Avian Influenza Virus (AIV) H5N1 is a highly pathogenic virus found not only in birds but also in human. Rapid and sensitive detection method is needed to help prevent the spread of AIV H5N1. In this study, a portable impedance biosensing system based on a laptop with LabVIEW software was developed for detection of AIV H5N1. First, a virtual instrument was programmed with LabVIEW software to form a platform for impedance measurement, data processing and control. The audio card of a laptop was used as a function generator while a data acquisition card was used with the signal channels for data communication. A gold interdigitated microelectrode was coated with specific aptamers to bind H5N1 virus and used in a microflow cell to obtain changes in impedance with desired accuracy and sensitivity. A sampling delivery unit consisted of a pump and three valves and was controlled by the virtual instrument to provide automated operation with adjustable flow rate. Results of the impedance measured with this biosensing system were compared with a commercial IM 6 impedance analyzer, and the error was less than 5%. The experiments on AIV H5N1 virus showed a linear relationship between the impedance change and the concentration of AIV H5N1 in a detection range from 2 to 16HAU.The specificity for detection of AIV H5N1 was confirmed with three non-target AIV subtypes, H1N1, H5N2, and H5N3.The biosensing system is portable and automated and has great potential to serve as a diagnostic and epidemiological tool for in-field rapid detection of AIV and other pathogens

Phonetic Temporal Neural Model for Language Identification

Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: it is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions.Comment: Submitted to TASL

A Study on Replay Attack and Anti-Spoofing for Automatic Speaker Verification

For practical automatic speaker verification (ASV) systems, replay attack poses a true risk. By replaying a pre-recorded speech signal of the genuine speaker, ASV systems tend to be easily fooled. An effective replay detection method is therefore highly desirable. In this study, we investigate a major difficulty in replay detection: the over-fitting problem caused by variability factors in speech signal. An F-ratio probing tool is proposed and three variability factors are investigated using this tool: speaker identity, speech content and playback & recording device. The analysis shows that device is the most influential factor that contributes the highest over-fitting risk. A frequency warping approach is studied to alleviate the over-fitting problem, as verified on the ASV-spoof 2017 database

Phone-aware Neural Language Identification

Pure acoustic neural models, particularly the LSTM-RNN model, have shown great potential in language identification (LID). However, the phonetic information has been largely overlooked by most of existing neural LID models, although this information has been used in the conventional phonetic LID systems with a great success. We present a phone-aware neural LID architecture, which is a deep LSTM-RNN LID system but accepts output from an RNN-based ASR system. By utilizing the phonetic knowledge, the LID performance can be significantly improved. Interestingly, even if the test language is not involved in the ASR training, the phonetic knowledge still presents a large contribution. Our experiments conducted on four languages within the Babel corpus demonstrated that the phone-aware approach is highly effective.Comment: arXiv admin note: text overlap with arXiv:1705.0315

Deep Speaker Feature Learning for Text-independent Speaker Verification

Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, either neural or probabilistic, has to be used to address the residual uncertainty when applied to speaker verification, just as with raw features. This paper presents a convolutional time-delay deep neural network structure (CT-DNN) for speaker feature learning. Our experimental results on the Fisher database demonstrated that this CT-DNN can produce high-quality speaker features: even with a single feature (0.3 seconds including the context), the EER can be as low as 7.68%. This effectively confirmed that the speaker trait is largely a deterministic short-time property rather than a long-time distributional pattern, and therefore can be extracted from just dozens of frames.Comment: deep neural networks, speaker verification, speaker featur