95 research outputs found
Discrete Wavelet Transforms
The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications
Enabling Technology in Optical Fiber Communications: From Device, System to Networking
This book explores the enabling technology in optical fiber communications. It focuses on the state-of-the-art advances from fundamental theories, devices, and subsystems to networking applications as well as future perspectives of optical fiber communications. The topics cover include integrated photonics, fiber optics, fiber and free-space optical communications, and optical networking
ワイヤレス通信のための先進的な信号処理技術を用いた非線形補償法の研究
The inherit nonlinearity in analogue front-ends of transmitters and receivers have had primary impact on the overall performance of the wireless communication systems, as it gives arise of substantial distortion when transmitting and processing signals with such circuits. Therefore, the nonlinear compensation (linearization) techniques become essential to suppress the distortion to an acceptable extent in order to ensure sufficient low bit error rate. Furthermore, the increasing demands on higher data rate and ubiquitous interoperability between various multi-coverage protocols are two of the most important features of the contemporary communication system. The former demand pushes the communication system to use wider bandwidth and the latter one brings up severe coexistence problems. Having fully considered the problems raised above, the work in this Ph.D. thesis carries out extensive researches on the nonlinear compensations utilizing advanced digital signal processing techniques. The motivation behind this is to push more processing tasks to the digital domain, as it can potentially cut down the bill of materials (BOM) costs paid for the off-chip devices and reduce practical implementation difficulties. The work here is carried out using three approaches: numerical analysis & computer simulations; experimental tests using commercial instruments; actual implementation with FPGA. The primary contributions for this thesis are summarized as the following three points: 1) An adaptive digital predistortion (DPD) with fast convergence rate and low complexity for multi-carrier GSM system is presented. Albeit a legacy system, the GSM, however, has a very strict requirement on the out-of-band emission, thus it represents a much more difficult hurdle for DPD application. It is successfully implemented in an FPGA without using any other auxiliary processor. A simplified multiplier-free NLMS algorithm, especially suitable for FPGA implementation, for fast adapting the LUT is proposed. Many design methodologies and practical implementation issues are discussed in details. Experimental results have shown that the DPD performed robustly when it is involved in the multichannel transmitter. 2) The next generation system (5G) will unquestionably use wider bandwidth to support higher throughput, which poses stringent needs for using high-speed data converters. Herein the analog-to-digital converter (ADC) tends to be the most expensive single device in the whole transmitter/receiver systems. Therefore, conventional DPD utilizing high-speed ADC becomes unaffordable, especially for small base stations (micro, pico and femto). A digital predistortion technique utilizing spectral extrapolation is proposed in this thesis, wherein with band-limited feedback signal, the requirement on ADC speed can be significantly released. Experimental results have validated the feasibility of the proposed technique for coping with band-limited feedback signal. It has been shown that adequate linearization performance can be achieved even if the acquisition bandwidth is less than the original signal bandwidth. The experimental results obtained by using LTE-Advanced signal of 320 MHz bandwidth are quite satisfactory, and to the authors’ knowledge, this is the first high-performance wideband DPD ever been reported. 3) To address the predicament that mobile operators do not have enough contiguous usable bandwidth, carrier aggregation (CA) technique is developed and imported into 4G LTE-Advanced. This pushes the utilization of concurrent dual-band transmitter/receiver, which reduces the hardware expense by using a single front-end. Compensation techniques for the respective concurrent dual-band transmitter and receiver front-ends are proposed to combat the inter-band modulation distortion, and simultaneously reduce the distortion for the both lower-side band and upper-side band signals.電気通信大学201
Next generation >200 Gb/s multicore fiber short-reach networks employing machine learning
This work proposes and evaluates the use of machine learning (ML) techniques on >200 Gb/s
short-reach systems employing weakly coupled multicore fiber (MCF) and Kramers-Kronig
(KK) receivers. The short-reach systems commonly found in intra data centers (DC)
connections demand low cost-efficient direct detection receivers (DD). The KK receivers allow
the combination of higher modulation order, such as 16-QAM used in coherent systems, with
the low complexity and low cost of DD. Thus, the use of KK receivers allows to increase the
bit rate and spectral efficiency while maintaining the cost of DD systems as this is an important
requirement in DC. The use of MCF allows to increase the system capacity as well as the system
cable density, although the use of MCF induces additional distortion, known as inter-core
crosstalk (ICXT), to the system. Thus, low complexity ML techniques such as k-means
clustering, k nearest neighbor (KNN) and artificial neural network (ANN) (estimation
feedforward neural network (FNN) and classification feedforward neural network) are
proposed to mitigate the effects of random ICXT.
The performance improvement provided by the k-means clustering, KNN and the two types
of FNN techniques is assessed and compared with the system performance obtained without
the use of ML. The use of estimation and classification FNN prove to significantly improve the
system performance by mitigating the impact of the ICXT on the received signal. This is
achieved by employing only 10 neurons in the hidden layer and four input features. It has been
shown that k-means or KNN techniques do not provide performance improvement compared
to the system without using ML. These conclusions are valid for direct detection MCF-based
short-reach systems with the product between the skew (relative time delay between cores) and
the symbol rate much lower than one (skew x symbol rate « 1). By employing the proposed
ANNs, the system shows an improvement of approximately 12 dB on the ICXT level, for the
same outage probability when comparing with the system without the use of ML. For the BER
threshold of 10−1.8
and compared with the standard system operating without employing ML
techniques, the system operating with the proposed ANNs show a received optical power
improvement of almost 3 dB and a ICXT level improvement of approximately 9 dB when the
mean BER is analized.Este trabalho propõe e avalia o uso de técnicas de machine learning (ML) em sistemas de curto alcance com ritmo binário superior a 200 Gb/s utilizando receptores Kramers-Kronig (KK) e fibras multinúcleo (MCF). Os sistemas de curto alcance usualmente encontrados em conexões
intra-data centers (DC) exigem receptores de deteção direta (DD) de baixo custo. Os receptores KK permitem a combinação de sistemas de modulação de maior ordem, tais como o 16-QAM, usado em sistemas coerentes, com o baixo custo dos receptores DD. Portanto, o uso de
receptores KK permite melhorar o ritmo binário e eficiência espectral e manter a eficiência de custo dos sistemas DD, o que é importante em DC. O uso de fibras multinúcleo permite o aumento da capacidade do sistema, bem como a densidade de cabos. No entanto, o uso de MCF
introduz uma distorção adicional no sistema conhecida como inter-core crosstalk (ICXT). Para mitigar os efeitos do ICXT aleatório, são propostas e avaliadas técnicas de ML de baixa complexidade como k-means clustering, k nearest neighbor (KNN) e rede neuronais artificiais
(ANN).
O desempenho associado à utilização de algoritmos de ML (k-means, KNN e duas redes neuronais do tipo feedforward (FNN): uma para estimação e outra para classificação), é avaliado e comparado com o desempenho do sistema obtido sem o uso de ML. A utilização de FNN para estimação e classificação conduziram a uma melhoria significativa no desempenho do sistema, mitigando o impacto do ICXT no sinal recebido. Isso é alcançado com o uso de uma rede neuronal com uma arquitetura muito simples contendo quatro entradas e 10 neurónios na camada escondida. Foi demonstrado que os algoritmos k-means e KNN não proporcionam melhoria de desempenho em comparação com o sistema sem o uso de ML. Essas conclusões são válidas para sistemas DD de curto alcance baseados em MCF com o produto entre o skew (atraso relativo entre os núcleos) e o ritmo de símbolos muito menor que um (skew x symbol rate « 1). Com o uso das ANNs, o sistema apresenta uma melhoria de aproximadamente 12 dB na probabilidade de indisponibilidade quando comparado com o sistema sem o uso de ML.
Para o limite de BER de 10−1.8 , e comparado com o sistema padrão sem o uso de técnicas de ML, o sistema com o uso de ANN mostra uma melhoria na potência óptica recebida de quase 3 dB e uma melhoria no nível de ICXT de aproximadamente 9 dB em relação ao BER médio
Effects of errorless learning on the acquisition of velopharyngeal movement control
Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio
Cognitive Radio Systems
Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems
On Applications of New Soft and Evolutionary Computing Techniques to Direct and Inverse Modeling Problems
Adaptive direct modeling or system identification and adaptive inverse modeling or channel equalization find extensive applications in telecommunication, control system, instrumentation, power system engineering and geophysics. If the plants or systems are nonlinear, dynamic, Hammerstein and multiple-input and multiple-output (MIMO) types, the identification task
becomes very difficult.
Further, the existing conventional methods like the least mean square (LMS) and recursive least square (RLS) algorithms do not provide satisfactory training to develop accurate direct and inverse models. Very often these (LMS and RLS) derivative based algorithms do not lead to optimal solutions in pole-zero and Hammerstein type system identification problem as they have tendency to be trapped by local minima.
In many practical situations the output data are contaminated with impulsive type outliers in addition to measurement noise. The density of the outliers may be up to 50%, which means that about 50% of the available data are affected by outliers. The strength of these outliers may be two to five times the maximum amplitude of the signal. Under such adverse conditions the available learning algorithms are not effective in imparting satisfactory training to update the weights of the adaptive models. As a result the resultant direct and inverse models become inaccurate and improper.
Hence there are three important issues which need attention to be resolved. These are :
(i) Development of accurate direct and inverse models of complex plants using some novel architecture and new learning techniques.
(ii) Development of new training rules which alleviates local minima problem during training and thus help in generating improved adaptive models.
(iii) Development of robust training strategy which is less sensitive to outliers in training and thus to create identification and equalization models which are robust
against outliers.
These issues are addressed in this thesis and corresponding contribution are outlined in seven Chapters. In addition, one Chapter on introduction, another on required architectures and algorithms and last Chapter on conclusion and scope for further research work are embodied
in the thesis.
A new cascaded low complexity functional link artificial neural network (FLANN) structure is proposed and the corresponding learning algorithm is derived and used to identify nonlinear dynamic plants. In terms of identification performance this model is shown to outperform the multilayer perceptron and FLANN model. A novel method of identification of IIR plants is proposed using comprehensive learning particle swarm optimization (CLPSO) algorithm. It is shown that the new approach is more accurate in identification and takes less CPU time
compared to those obtained by existing recursive LMS (RLMS), genetic algorithm (GA) and PSO based approaches. The bacterial foraging optimization (BFO) and PSO are used to develop efficient learning algorithms to train models to identify nonlinear dynamic and MIMO plants. The new scheme takes less computational effort, more accurate and consumes less input samples for training. Robust identification and equalization of complex plants have been carried out using outliers in training sets through minimization of robust norms using PSO and BFO based methods. This method yields robust performance both in equalization and identification tasks. Identification of Hammerstein plants has been achieved successfully using PSO, new clonal PSO (CPSO) and immunized PSO (IPSO) algorithms. Finally the thesis proposes a distributed approach to identification of plants by developing two distributed learning algorithms : incremental PSO and diffusion PSO. It is shown that the new approach is more efficient in terms of accuracy and training time compared to centralized PSO based approach. In addition a robust distributed approach for identification is proposed and its performance has been evaluated.
In essence the thesis proposed many new and efficient algorithms and structure for identification and equalization task such as distributed algorithms, robust algorithms, algorithms for ploe-zero identification and Hammerstein models. All these new methods are shown to be better in terms of performance, speed of computation or accuracy of results
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