Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation

This paper proposes the use of variational Kalman filtering as an inference technique for adaptive classification in a brain computer interface (BCI). The proposed algorithm translates electroencephalogram segments adaptively into probabilities of cognitive states. It, thus, allows for nonstationarities in the joint process over cognitive state and generated EEG which may occur during a consecutive number of trials. Nonstationarities may have technical reasons (e.g., changes in impedance between scalp and electrodes) or be caused by learning effects in subjects. We compare the performance of the proposed method against an equivalent static classifier by estimating the generalization accuracy and the bit rate of the BCI. Using data from two studies with healthy subjects, we conclude that adaptive classification significantly improves BCI performance. Averaging over all subjects that participated in the respective study, we obtain, depending on the cognitive task pairing, an increase both in generalization accuracy and bit rate of up to 8%. We may, thus, conclude that adaptive inference can play a significant contribution in the quest of increasing bit rates and robustness of current BCI technology. This is especially true since the proposed algorithm can be applied in real time.     

Fuzzy image clustering incorporating spatial continuity

The authors present a spatial fuzzy clustering algorithm that exploits the spatial contextual information in image data. The objective functional of their method utilises a new dissimilarity index that takes into account the influence of the neighbouring pixels on the centre pixel in a 3×1 window. The algorithm is adaptive to the image content in the sense that influence from the neighbouring pixels is suppressed in nonhomogeneous regions in the image. A cluster merging scheme that merges two clusters based on their closeness and their degree of overlap is presented. Through this merging scheme, an `optimal' number of clusters can be determined automatically as iteration proceeds. Experimental results with synthetic and real images indicate that the proposed algorithm is more tolerant to noise, better at resolving classification ambiguity and coping with different cluster shape and size than the conventional fuzzy c-means algorithm     

Real-time discrimination of ventricular tachyarrhythmia withFourier-transform neural network

The authors have developed a method to discriminate life-threatening ventricular arrhythmias by observing the QRS complex of the electrocardiogram (ECG) in each heartbeat. Changes in QRS complexes due to rhythm origination and conduction path were observed with the Fourier transform, and three kinds of rhythms were discriminated by a neural network. In this paper, the potential of the authors' method for clinical uses and real-time detection was examined using human surface ECGs and intracardiac electrograms (EGMs). The method achieved high sensitivity and specificity (>0.98) in discrimination of supraventricular rhythms from ventricular ones. The authors also present a hardware implementation of the algorithm on a commercial single-chip CPU     

基于帧间相关性的自适应采样率分块视频压缩感知

针对现有基于自适应采样率的分块视频压缩感知方案的单帧总采样率不可控的问题,提出了一种新的自适应采样率分配方案。首先,对当前帧图像块进行固定预采样;然后,根据预采样的测量值来估计图像块的变化程度,并计算该图像块与当前帧图像的复杂度比例;接下来,根据复杂度比例分配图像块自适应采样率,并将固定预采样及自适应采样的测量值合并为最终测量值。实验结果表明,与固定采样率算法相比,提出的方案在相同采样率下可获得1 dB左右的峰值信噪比增益。所提方案可获得高质量的重构图像,且总采样率可控,因此增强了自适应采样分块视频压缩感知方案的有效性和实用性。     

An integral equation model for intracardiac electrogram sensing

The electrogram sensed by an intracardiac electrode has long been characterized based on two approaches: (1) presuming that the electrode is very small and does not disturb the potential prior to applying the electrode; and (2) taking an average of the prior potential over the electrode surface. In fact, any intracardiac sensing electrode has a finite surface area where electrical charges are induced and disturb the external potential field, thus, the sensed potential is different from the potential prior to placing the electrode. In this paper, an integral equation model is proposed based on the current continuity equation in a homogeneous myocardial medium. The new model can accurately characterize the electrogram sensed by an electrode with a nonnegligible surface area and a load impedance. The new model can be solved numerically via the method of moments to obtain the potential induced on the electrode surface by an arbitrary dipole volume source. As an application of the proposed theory, several electrode configurations with different loads have been analyzed with an intent to show that a finite electrode surface will significantly reduce the electrogram peak amplitude and slope, and a load impedance lower than 20 kΩ will also degrade the electrogram sensitivity     

Adaptive Rate Optimal Design for Wireless Heterogeneous Networks: Jointly Congestion Discrimination and Queue Updating

In this paper, we propose a novel rate adaptive optimization scheme for streaming media transmission over wireless heterogeneous IP networks. In the proposed adaptive scheme, through the analysis of the packet loss characteristics in wireless channel, we develop the relationship between the packet loss rates and the packet sizes. Furthermore, the scheme detects the nature of packet losses by sending large and small packets alternately, and then adopts an adaptive rate optimization strategy to decrease the network congestion and increase the network throughput. Using congestion discrimination and updating factor, the scheme can adapt to the changes of network states quickly and improve delivery quality of wireless multimedia streaming. Simulation results show that, in comparisons to the existing rate optimization algorithms, our proposed scheme offers significantly improved performance in terms of throughput and network congestion, especially when the channel quality is poor in different network topology environments.     

Polar co-ordinates shape adaptive discrete transform-basedwatermarking scheme for arbitrarily-shaped object

A new watermarking scheme for arbitrarily-shaped image is proposed. Using co-ordinates conversion to log-polar and polar co-ordinates shape adaptive discrete transform (PSADT), the proposed watermarking method can be applied to any arbitrarily-shaped image such as MPEG-4 VOP and is much more robust to geometrical attacks than conventional ones     

基于模糊逻辑的无线网络流量自适应控制

文章在分析现有提高无线TCP性能方案的基础上，提出一种新的流量控制方法，即基于显式窗口反馈的无线网络流量控制方案。在Snoop中引入有线网络的显示窗口自适应（EWA）算法，通过对BS共享缓存的实时监测，应用模糊控制算法预测当前拥塞窗口（cwnd）的大小，并显式反馈给发送端，使TCP的发送窗口能快速响应网络负荷状况的变化．避免分组的丢失。仿真结果表明．该方法增强了网络对拥塞的自适应性以及对无线信道差错的实时处理能力．提高了网络的吞吐量。     

Adaptive retransmission control for slotted frequency-hoppedspread-spectrum multiple access systems

The authors propose a simple adaptive retransmission control scheme for slotted frequency-hopped spread-spectrum multiple access systems. The scheme changes the retransmission probability adaptively according to the system state such that channel throughput can be maximised. It is shown that a performance quite close to the maximum possible throughput can be achieved     

Selection of myocardial electrogram features for use by implantabledevices

Implantable devices that terminate ventricular tachycardia must be capable of correctly classifying heart rhythms to a high degree of reliability. The relative discriminating power of several myocardial electrogram features in six human subjects were evaluated by reducing the order of their corresponding feature spaces using three different optimization methods: minimizing univariate Bayes error rates (univariate parametric), maximizing the Kullback divergence (multivariate parametric), and pruning classification trees (nonparametric). It was found that although the composition of the optimal subspaces varied considerably from one subject to another, one frequency domain feature was common to most of the optimal subspaces     

An adaptive classifier design for high-dimensional data analysiswith a limited training data set

Proposes a self-learning and self-improving adaptive classifier to mitigate the problem of small training sample size that can severely affect the recognition accuracy of classifiers when the dimensionality of the multispectral data is high. This proposed adaptive classifier utilizes classified samples (referred to as semilabeled samples) in addition to original training samples iteratively. In order to control the influence of semilabeled samples, the proposed method gives full weight to the training samples and reduced weight to semilabeled samples. The authors show that by using additional semilabeled samples that are available without extra cost, the additional class label information may be extracted and utilized to enhance statistics estimation and hence improve the classifier performance, and therefore the Hughes phenomenon (peak phenomenon) may be mitigated. Experimental results show this proposed adaptive classifier can improve the classification accuracy as well as representation of estimated statistics significantly     

A reversible steganographic scheme for VQ indices based on locally adaptive coding

Achieving a high embedding capacity and low compression rate with a reversible data hiding method in the vector quantization (VQ) compressed domain is a technically challenging problem. This paper proposes a novel reversible steganographic scheme for VQ compressed images based on a locally adaptive data compression method. The proposed method embeds n secret bits into one VQ index of an index table in Hilbert-curve scan order. The experimental results show that the proposed method can achieve the different average embedding rates of 0.99, 1.68, 2.28, and 3.04 bit per index (bpi) and average compression rates of 0.45, 0.46, 0.5, and 0.56 bit per pixel (bpp) for n = 1, 2, 3, and 4, respectively. These results indicate that the proposed scheme is superior to Chang et al.’s scheme 1 [19], Yang and Lin’s scheme [21], and Chang et al.’s scheme 2 [24].     

Adaptive trellis-coded multiple-phase-shift keying for Rayleighfading channels

An adaptive scheme for trellis-coded modulation of MPSK signals, called adaptive trellis-coded multiple-phase-shift keying (ATCMPSK), is proposed for slowly Rayleigh fading channels. The adaptive scheme employs a slightly modified rate 1/2 convolutional encoder and the corresponding Viterbi decoder to realize a family of codes of different rates which are employed according to channel conditions. During poor channel conditions, trellis-coded QPSK (TCQPSK) together with repetition schemes are employed. As channel conditions improve, higher rate schemes such as trellis-coded 16 PSK are used. An interleaving/deinterleaving method suitable for the adaptive scheme is proposed. Theoretical bounds for the error performance and an exact expression for the throughput of the proposed adaptive scheme are derived, and are compared against simulation results. Simulations have been performed to measure the performance of the scheme for different parameters and some nonideal conditions. It is shown that ATCMPSK results in considerable improvement in bit-error-rate (BER) performance of MPSK signals. Under ideal conditions, gains in the range of 3-20 dB are achieved over conventional fixed rate pragmatic trellis-coded schemes     

一种自适应采样率视频压缩感知方案

为了进一步提高视频压缩感知方案的重构图像质量,提出了一种新的自适应采样方案.在该方案中,根据不同图像块的稀疏度自适应分配采样率.在对各图像块分类判决时,首先判断图像块在离散余弦变换域的稀疏度,其次结合该图像块与其参考帧之间的时域相关性,确定图像块的分类.实验结果表明,与现有的自适应采样率分配方案相比,该算法可获得0.5 dB左右的峰值信噪比增益.     

用增益精确值和归一化波形码书改进G.728

分析研究了增益、波形乘积码书结构的缺陷，设计了归一化波形码书和精确表示增益的LD-CELP方案。采用自适应预测和自适应量化对增益的精确值进行量化，在3bit和4bit自适应量化时比G．728固定量化增益分别提高0．5dB和6dB。采用4bit自适应量化和64波形码书比G.728SNR提高约1dB。将G．728综合滤波器由50阶减少到30阶，信噪比不变而算法复杂性降低约20%。     

Orientation adaptive subband coding of images

In the subband coding of images, directionality of image features has thus far been exploited very little. The proposed subband coding scheme utilizes orientation of local image features to avoid the highly objectionable Gibbs-like phenomena observed at reconstructed image edges with conventional subband schemes at low bit rates, At comparable bit rates, the subjective image quality obtained by our orientation adaptive scheme is considerably enhanced over a conventional separable subband coding scheme, as well as other separable approaches such as the JPEG compression standard.     

Supervised classification of K-distributed SAR images of naturaltargets and probability of error estimation

A radiometric and textural classification method for the single-channel synthetic aperture radar (SAR) image is proposed, which explicitly takes into account the probability density function (pdf) of the underlying cross section for K-distributed images. This method makes extensive use of adaptive preprocessing methods (e.g. Gamma-Gamma MAP speckle filtering), in order to ensure good classification accuracy as well as fair preservation of the spatial resolution of the final result. Error rates can be estimated during the training step, allowing one to select only relevant reflectivity classes and to save computation time in trials. The classification method is based on a maximum likelihood (ML) segmentation of the filtered image. Finally, a texture criterion is introduced to improve the accuracy of the classification result     

Joint Source and Channel Coding using Punctured Ring Convolutional Coded CPM

In this paper, a novel trellis source encoding scheme based on punctured ring convolutional codes is presented. Joint source and channel coding (JSCC) using trellis coded continuous phase modulation (CPM) with punctured convolutional codes over rings is investigated. The channels considered are the additive white gaussian noise (AWGN) channel and the Rayleigh fading channel. Optimal soft decoding for the proposed JSCC scheme is studied. The soft decoder is based on the a posteriori probability (APP) algorithm for trellis coded CPM with punctured ring convolutional codes. It is shown that these systems with soft decoding outperform the same systems with hard decoding especially when the systems operate at low to medium signal-to-noise ratio (SNR). Furthermore, adaptive JSCC approaches based on the proposed source coding scheme are investigated. Compared with JSCC schemes with fixed source coding rates, the proposed adaptive approaches can achieve much better performance in the high SNR region. The novelties of this work are the development of a trellis source encoding method based on punctured ring convolutional codes, the use of a soft decoder, the APP algorithm for the combined systems and the adaptive approaches to the JSCC problem.     

基于总错误率和特征关联的自适应融合多模态生物特征识别

针对单生物特征识别准确率和鲁棒性差的问题, 提出了一种基于总错误率(TER)和特征关联自适应融合多模态生物特 征识别方法。首先将TER作为判别特征引入到多模态识别,以代替传统的匹配分 数;其次在不确定度量理论的基 础上,考虑人脸特征和语音特征之间的时空关联性,提出了一种基于特征关联的多特征 自适应融合策略,利用特征关联 系数自适应调节不同识别特征对识别结果的贡献。仿真实验表明,与几种代表性的融合算法 相比,本文所 提出的融合模式可以有效提高多生物特征识别系统的准确性和鲁棒性。     

Block-Based Adaptive Image Steganography Using LSB Matching Revisited

Recently, an edge adaptive image steganographic method based on least significant bit (LSB) matching revisited (EA-LSBMR) has been proposed, which holds good visual quality and proper security under appropriate embedding rates. However, from the extensive experiments to EA-LSBMR, we find that the discrete Fourier transform (DFT) spectrum of pixelpairs differences histogram still reveals the presence of a secret message even in a low embedding rate. To enhance the security, a modified scheme is proposed in this paper, which can defeat the above-mentioned analysis and keep the visual quality better than EA-LSBMR in higher embedding rates. Experimental results using a latest universal steganalysis method have demonstrated the proposed method's good performance.