A Multilevel Coded Modulation Approach for Hexagonal Signal Constellation

We propose a new coded modulation called hexagonal shell modulation (HSM). The HSM has a signal constellation composed of shell-like tiling of hexagons and thus has a lower peak-to-average power ratio (PAR) than a standard square quadrature amplitude modulation (QAM) with comparable bandwidth efficiency and minimum Euclidean distance. The main challenge is that HSM has a non-power-of-two number of constellation points, and thus assignment of binary information to HSM is not straightforward. We resolve this by applying a multilevel coded modulation (MLC) scheme where a ternary set partitioning combined with binary-input ternary-output (BITO) turbo codes is employed to fully exploit the property of the nonpower- of-two constellation points. Throughout this letter, we focus on an 18-ary HSM with the information rate of 3 bit/symbol as a specific example. It is shown that this system outperforms the standard square 16-QAM with the same rate when PAR is constrained.     

Maximizing the data transmission rate of a cooperative relay system in an underwater acoustic channel

In this paper, we investigate the problem of maximizing the data transmission rate of a cooperative relay system in an underwater acoustic communication channel. With amplify‐and‐forward relaying and adaptive source transmission, we present optimal transmit signal power adaptation policies that maximize the data transmission rate, considering both frequency and time domains. The analysis takes into account a physical model of acoustic path loss and ambient noise power spectral density. Typical characteristics of underwater channel such as frequency‐dependent fading and time variations are also considered. Capacity bounds for channel state information (CSI) only at the receiver and CSI at both transmitter and receiver are presented. To maximize the data rate, we use the notion of an optimal bandwidth which corresponds to efficient allocation of signal power across the transmission bandwidth. Under the constraint of an average transmit power, the optimal transmit power adaptation policy is found to be ‘water‐pouring’ in frequency‐time domain, while the transmit power adaptation policy with a total power constraint is ‘water‐pouring’ in frequency domain. Results show that both frequency domain and frequency‐time domain power adaptation schemes provide much greater improvement in average data rate over that of the constant power case. Copyright © 2011 John Wiley & Sons, Ltd.     

Pilot-assisted wireless transmissions: general model, design criteria, and signal processing

Pilot-assisted transmission (PAT) multiplexes the known symbols with the information bearing data. These pilot symbols and the specific multiplexing scheme are known at the receiver and can be exploited for channel estimation, receiver adaptation, and optimal decoding. Even though PAT has been used for many practical reasons, there still remains a need for an optimal design. The theory and methodology for the design of an optimal PAT have emerged, but much still remains unknown. In this article, the author has presented an overview of PAT. A general PAT model is given as well as the review of the common design criteria. The information theoretic and signal processing issues were also discussed.     

Nonlocal PDE morphology: a generalized shock operator on graph

This paper presents an adaptation and extension of the shock filters on weighted graphs using the formalism of partial difference equations. This adaptation leads to new morphological operators that alternate between nonlocal dilation and nonlocal erosion filter type on graphs. Furthermore, this adaptation extends the shock filter applications to any data that can be represented by graphs. This approach is illustrated through image, data defined on region maps, and 3D point cloud processing.     

Automatic model-based segmentation of the heart in CT images

Automatic image processing methods are a prerequisite to efficiently analyze the large amount of image data produced by computed tomography (CT) scanners during cardiac exams. This paper introduces a model-based approach for the fully automatic segmentation of the whole heart (four chambers, myocardium, and great vessels) from 3-D CT images. Model adaptation is done by progressively increasing the degrees-of-freedom of the allowed deformations. This improves convergence as well as segmentation accuracy. The heart is first localized in the image using a 3-D implementation of the generalized Hough transform. Pose misalignment is corrected by matching the model to the image making use of a global similarity transformation. The complex initialization of the multicompartment mesh is then addressed by assigning an affine transformation to each anatomical region of the model. Finally, a deformable adaptation is performed to accurately match the boundaries of the patient's anatomy. A mean surface-to-surface error of 0.82 mm was measured in a leave-one-out quantitative validation carried out on 28 images. Moreover, the piecewise affine transformation introduced for mesh initialization and adaptation shows better interphase and interpatient shape variability characterization than commonly used principal component analysis.      

Tracking Nonstationary Visual Appearances by Data-Driven Adaptation

Without any prior about the target, the appearance is usually the only cue available in visual tracking. However, in general, the appearances are often nonstationary which may ruin the predefined visual measurements and often lead to tracking failure in practice. Thus, a natural solution is to adapt the observation model to the nonstationary appearances. However, this idea is threatened by the risk of adaptation drift that originates in its ill-posed nature, unless good data-driven constraints are imposed. Different from most existing adaptation schemes, we enforce three novel constraints for the optimal adaptation: (1) negative data, (2) bottom-up pair-wise data constraints, and (3) adaptation dynamics. Substantializing the general adaptation problem as a subspace adaptation problem, this paper presents a closed-form solution as well as a practical iterative algorithm for subspace tracking. Extensive experiments have demonstrated that the proposed approach can largely alleviate adaptation drift and achieve better tracking results for a large variety of nonstationary scenes.     

Competitive learning in asynchronous-pulse-density integrated circuits

This paper introduces MOS circuits for the integrated implementation of competitive learning. A singlelayer competitive network architecture composed of asynchronous-pulse-coded processing elements is employed. The processing elements perform analog computations and communicate via asynchronous-pulse-density-modulated signals which encode continuous-time signal information using discrete binary values. The specific focus is upon the design and simulation of an adaptive synapse circuit which combines a capacitive analog storage element with subthreshold adaptation circuitry. The simulations presented verify circuit operation in a two-input, three-output competitive network. Accurate clustering of both fixed and random training data is demonstrated.     

A simple adaptive algorithm for real-time processing in antenna arrays

A new adaptation algorithm designed for real-time data processing in large antenna arrays is presented. The algorithm is used to determine the set of filter coefficients (weights) which minimizes the mean-square error in a multidimensional linear filter. The algorithm forms an estimate of the target signal, which is assumed to be of interest, in the presence of interfering noises. It is assumed that the direction of arrival and spectral density of the target signal are known a priori. No such information is assumed to be available regarding the structure of the interfering noise field. The a priori target information is incorporated directly into the adaptation procedure using a modified gradient descent technique. The mathematical convergence properties of the algorithm are presented and a computer simulation experiment is used as an illustration. It is shown that as the number of iterations becomes large, the expected value of the adaptive solution converges to the minimum mean-square-error solution. It is further shown that the variance of the adapted filter about the optimum solution can be made arbitrarily small by appropriate choice of a scalar constant in the algorithm. These results are based on the assumption that the array signals are Gaussian and that successive time samples are statistically uncorrelated. Thus, the new algorithm is shown to converge to the optimum processor in the limit as the number of adaptations becomes large. Any disadvantage which may arise in the use of such an asymptotically optimum system is offset by the extreme simplicity of the adaptive procedure. This simplicity should prove to be particularly useful in many of the practical array processing problems recently encountered in seismic and sonar data processing.     

一种开放式地理信息系统开发平台的设计

为了便于大量动态目标实时地理信息系统的开发,基于开放式地理信息系统,通过系统体系结构和数据处理2方面的设计,给出一种跨操作系统的开放式地理信息系统开发平台的设计方案。该方案所设计的开发平台具有良好的后台数据处理能力和动态刷新性能,同时具有较强的可靠性、开放性和一定的实用价值。     

Channel quality estimation and rate adaptation for cellular mobileradio

We propose a technique to measure channel quality in terms of signal-to-interference plus noise ratio (SINR) for the transmission of signals over fading channels. The Euclidean distance (ED) metric, associated with the decoded information sequence or a suitable modification thereof, is used as a channel quality measure. Simulations show that the filtered or averaged metric is a reliable channel quality measure which remains consistent across different coded modulation schemes and at different mobile speeds. The average scaled ED metric can be mapped to the SINR per symbol. We propose the use of this SINR estimate for data rate adaptation, in addition to mobile assisted handoff (MAHO) and power control. We particularly focus on data rate adaptation and propose a set of coded modulation schemes which utilize the SINR estimate to adapt between modulations, thus improving the data throughput. Simulation results show that the proposed metric works well across the entire range of Dopplers to provide near-optimal rate adaptation to average SINR. This method of adaptation averages out short-term variations due to Rayleigh fading and adapts to the long-term effects such as shadowing. At low Dopplers, the metric can track Rayleigh fading and match the rate to a short-term average of the SINR, thus further increasing throughput     

On adaptive decision rules and decision parameter adaptation forautomatic speech recognition

Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing     

Frame-synchronous noise compensation for hands-free speechrecognition in car environments

It has become increasingly important to develop hands-free speech recognition techniques for the human-computer interface in car environments. However, severe car noise degrades the speech recognition performance substantially. To compensate the performance loss, it is necessary to adapt the original speech hidden Markov models (HMMs) to meet changing car environments. A novel frame-synchronous adaptation mechanism for in-car speech recognition is presented. This mechanism is intended to perform unsupervised model adaptation efficiently on a frame-by-frame basis instead of a conventional adaptation algorithm relying on batch adaptation data and supervision information. The proposed adaptation scheme is performed during frame likelihood calculation where an optimal equalisation factor is first computed to equalise the model mean vector and the input frame vector. This equalisation factor then serves as a reference index to retrieve an additional bias vector for model mean adaptation. As a result, a rapid and flexible algorithm is exploited to establish a new robust likelihood measure. In experiments on hands-free in-car speech recognition with the microphone far from the talker, this framework is found to be effective in terms of recognition rate and computational cost under various driving speeds     

Model predictive control of sensorless hybrid stepper motors in auxiliary adjuster for stereotactic frame fixation

A hybrid stepper motor (HSM) can be practically driven by open-loop control, however, it is definitely not enough for the increasing demands on precision control in wide industry applications. In this study, a practical control scheme, consisting of model predictive control (MPC) and extended Kalman filter (EKF), is presented for controlling a sensorless hybrid stepper motor (HSM). Numerical simulations and real-world experiments are conducted to show the performance and effectiveness of the proposed strategy. To extend the practical usage of the presented design, the proposed strategy has been utilized to control an auxiliary adjuster for stereotactic frame fixation system for use in the precise clinical neurosurgery operation.     

Modeling and control of expressiveness in music performance

Expression is an important aspect of music performance. It is the added value of a performance and is part of the reason that music is interesting to listen to and sounds alive. Understanding and modeling expressive content communication is important for many engineering applications in information technology. For example, in multimedia products, textual information is enriched by means of graphical and audio objects. In this paper, we present an original approach to modify the expressive content of a performance in a gradual way, both at the symbolic and signal levels. To this purpose, we discuss a model that applies a smooth morphing among performances with different expressive content, adapting the audio expressive character to the user's desires. Morphing can be realized with a wide range of graduality (from abrupt to very smooth), allowing adaptation of the system to different situations. The sound rendering is obtained by interfacing the expressiveness model with a dedicated postprocessing environment, which allows for the transformation of the event cues. The processing is based on the organized control of basic audio effects. Among the basic effects used, an original method for the spectral processing of audio is introduced.     

Adaptive Data Transmission Control for Multilane‐Based Ethernet

We propose a reconciliation sublayer (RS)‐based lane and traffic control protocol for energy‐efficient 40‐G/100‐G Ethernet. The RS performs active/inactive lane control and data rate adaptation depending on active lane information received from the upper layer. This protocol does not result in a processing delay in the media access control layer, nor is an additional buffer required at the physical layer for dynamic lane control. It ensures minimal delay and no overhead for the exchange of control frames and provides a simple adaptive data rate.     

高校学生全信息查询通用信息模型的研究与实现

高校学生综合数据查询中,建立信息查询通用的信息模型至关重要。提出了通用的全信息查询信息模型。基于对现实需求的分析,提出基础结构数据和卡片结构数据2种概念,并基于此归纳出模型的数据项、数据视图、用户角色、管理级别和源权限5种基本元素与角色层、数据层、过滤层3层结构。针对模型实现中的数据视图建立、数据源整合方式、数据项呈现等6项问题进行重点阐述。实践表明,模型具有高度适配性和实用性,可以基本解决高校综合查询中如数据项权限、人员角色分类、行政级别权限、多种数据源等问题。     

The PIM architecture for wide-area multicast routing

The purpose of multicast routing is to reduce the communication costs for applications that send the same data to multiple recipients. Existing multicast routing mechanisms were intended for use within regions where a group is widely represented or bandwidth is universally plentiful. When group members, and senders to those group members, are distributed sparsely across a wide area, these schemes are not efficient; data packets or membership report information are occasionally sent over many links that do not lead to receivers or senders, respectively. We have developed a multicast routing architecture that efficiently establishes distribution trees across wide area internets, where many groups will be sparsely represented. Efficiency is measured in terms of the router state, control message processing, and data packet processing, required across the entire network in order to deliver data packets to the members of the group. Our protocol independent multicast (PIM) architecture: (a) maintains the traditional IP multicast service model of receiver-initiated membership, (b) supports both shared and source-specific (shortest-path) distribution trees, (c) is not dependent on a specific unicast routing protocol, and (d) uses soft-state mechanisms to adapt to underlying network conditions and group dynamics. The robustness, flexibility, and scaling properties of this architecture make it well-suited to large heterogeneous internetworks     

子域自适应网络跨被试情绪识别算法

为解决由于脑电信号（EEG）的非平稳性及个体差异性造成的情绪识别模型在不同时间、不同被试间泛化性能低的问题,提出全局域适应与相关子域自适应串联系统（SS＿GDAN＿RSAN）模型来实现跨被试的情感识别。将整个情感识别模型分为特征提取器、全局域分类器和子域域分类器。首先在浅层神经网络中由特征提取器和全局域分类器产生域不变表达,通过最小化源域数据分类损失及源域与目标域数据的分布差异损失进行全局域自适应;其次在深层神经网络中,基于局部最大平均差异度量源域和目标域中相关子域数据的分布差异,通过最小化源域数据分类损失和子域自适应损失训练子域域分类器,进而捕获每个类别的细粒度信息实现子域自适应。实验结果表明SS＿GDAN＿RSAN算法简单有效,在多对一的跨被试迁移实验中识别率达到84.05%±5.91%,在单被试跨时间迁移实验中识别率达到91.66%±7.32%。与传统分类器模型相比,SS＿GDAN＿RSAN对跨被试、跨时间情绪分类任务泛化能力的提高取得显著效果。     

Perceived Risk and Self-Efficacy as Motivators: Understanding Individuals' Long-Term Use of Health Information

Using Witte's (1992) extended parallel process model, this study identifies 4 groups of individuals according to their perceived risk and self-efficacy: responsive (high perceived risk, high efficacy), proactive (low perceived risk, high efficacy), avoidance (high perceived risk, low efficacy), and indifference (low perceived risk, low efficacy). Membership in these groups is hypothesized to influence motivation to think about cardiovascular diseases (CVD), use of CVD-related information, and knowledge acquisition. In the cross-sectional data waves, there was a significant interaction between risk perception and self-efficacy on individuals' (a) motivation to think about CVD issues, (b) use of health information, and (c) knowledge acquisition. This study also found similar results longitudinally over a 2-year and a 6-year period.