Packet data communications over coded CDMA with hybrid type-II ARQ

This dissertation presents in-depth investigation of turbo-coded CDNIA systems in packet data communication terminology. It is divided into three parts; (1) CDMA with hybrid FEC/ARQ in deterministic environment, (2) CDMA with hybrid FEC/ARQ in random access environment and (3) an implementation issue on turbo decoding. As a preliminary, the performance of CDMA with hybrid FEC/ARQ is investigated in deterministic environment. It highlights the practically achievable spectral efficiency of CDMA system with turbo codes and the effect of code rates on the performance of systems with MF and LMMSE receivers, respectively. For given ensemble distance spectra of punctured turbo codes, an improved union bound is used to evaluate the error probability of ML turbo decoder with MF receiver and with LMMSE receiver front-end and, then, the corresponding spectral efficiency is computed as a function of system load. In the second part, a generalized analytical framework is first provided to analyze hybrid type-11 ARQ in random access environment. When applying hybrid type-11 ARQ, probability of packet success and packet length is generally different from attempt to attempt. Since the conventional analytical model, customarily employed for ALOHA system with pure or hybrid type-I ARQ, cannot be applied for this case, an expanded analytical model is introduced. It can be regarded as a network of queues and Jackson and Burke\u27s theorems can be applied to simplify the analysis. The second part is further divided into two sub topics, i.e. CDMA slotted ALOHA with hybrid type-11 ARQ using packet combining and CDMA unslotted ALOHA with hybrid type-11 ARQ using code combining. For code combining, the rate compatible punctured turbo (RCPT) codes are examined. In the third part, noticing that the decoding delay is crucial to the fast ARQ, a parallel MAP algorithm is proposed to reduce the computational decoding delay of turbo codes. It utilizes the forward and backward variables computed in the previous iteration to provide boundary distributions for each sub-block MAP decoder. It has at least two advantages over the existing parallel scheme; No performance degradation and No additional computation

Multimodal Speech Emotion Recognition Using Audio and Text

Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers. In this paper, we propose a novel deep dual recurrent encoder model that utilizes text data and audio signals simultaneously to obtain a better understanding of speech data. As emotional dialogue is composed of sound and spoken content, our model encodes the information from audio and text sequences using dual recurrent neural networks (RNNs) and then combines the information from these sources to predict the emotion class. This architecture analyzes speech data from the signal level to the language level, and it thus utilizes the information within the data more comprehensively than models that focus on audio features. Extensive experiments are conducted to investigate the efficacy and properties of the proposed model. Our proposed model outperforms previous state-of-the-art methods in assigning data to one of four emotion categories (i.e., angry, happy, sad and neutral) when the model is applied to the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.Comment: 7 pages, Accepted as a conference paper at IEEE SLT 201

A Low Complexity MIMO Detection Based on Pair-Wise Markov Random Fields

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Speech Emotion Recognition Using Multi-hop Attention Mechanism

In this paper, we are interested in exploiting textual and acoustic data of an utterance for the speech emotion classification task. The baseline approach models the information from audio and text independently using two deep neural networks (DNNs). The outputs from both the DNNs are then fused for classification. As opposed to using knowledge from both the modalities separately, we propose a framework to exploit acoustic information in tandem with lexical data. The proposed framework uses two bi-directional long short-term memory (BLSTM) for obtaining hidden representations of the utterance. Furthermore, we propose an attention mechanism, referred to as the multi-hop, which is trained to automatically infer the correlation between the modalities. The multi-hop attention first computes the relevant segments of the textual data corresponding to the audio signal. The relevant textual data is then applied to attend parts of the audio signal. To evaluate the performance of the proposed system, experiments are performed in the IEMOCAP dataset. Experimental results show that the proposed technique outperforms the state-of-the-art system by 6.5% relative improvement in terms of weighted accuracy.Comment: 5 pages, Accepted as a conference paper at ICASSP 2019 (oral presentation

Graphitic carbon growth on crystalline and amorphous oxide substrates using molecular beam epitaxy

We report graphitic carbon growth on crystalline and amorphous oxide substrates by using carbon molecular beam epitaxy. The films are characterized by Raman spectroscopy and X-ray photoelectron spectroscopy. The formations of nanocrystalline graphite are observed on silicon dioxide and glass, while mainly sp2 amorphous carbons are formed on strontium titanate and yttria-stabilized zirconia. Interestingly, flat carbon layers with high degree of graphitization are formed even on amorphous oxides. Our results provide a progress toward direct graphene growth on oxide materials

Growth direction determination of a single RuO2 nanowire by polarized Raman spectroscopy

The dependence of band intensities in the Raman spectrum of individual single-crystal ruthenium dioxide (RuO2) nanowires on the angle between the plane of polarization of the exciting (and collected) light and the long axis of the nanowire, is shown to be a simple, complementary technique to high resolution transmission electron microscopy (HRTEM) for determining nanowire growth direction. We show that excellent agreement exists between what is observed and what is predicted for the polarization angle dependence of the intensities of the nanowires' E-g (525 cm(-1)) and the B-2g (714 cm(-1)) Raman bands, only by assuming that the nanowires grow along the (001) crystallographic direction, as confirmed by HRTEM.open9

Hierarchically assembled 1-dimensional hetero-nanostructures: single crystalline RuO2 nanowires on electrospun IrO2 nanofibres

We report a facile growth route to synthesize hierarchically grown single crystalline metallic RuO2 nanowires on electrospun IrO2 nanofibres via a combination of a simple vapour phase transport process with an electrospinning process. This synthetic strategy could be extended to design a variety of highly branched network architectures of functional hetero-nanostructures with possible future applications.close4