Analog on-chip-learning for active noise canceling
Active noise canceling is demonstrated by analog neuro-chips with on-chip learning capability. The developed neuro-chip faithfully incorporates the error-backpropagation learning rule. Without any digital signal processor the developed system successfully compensated for nonlinear distortion of loudspeakers as well as for acoustic multi-path fading and random noises in real time.

title: Training Algorithm with Incomplete Data for Feed-Forward Neural Networks
abstract: A new algorithm is developed to train feed-forward neural networks for non-linear input-to-output mappings with small incomplete data in arbitrary distributions. The developed Training-EStimation-Training (TEST) algorithm consists of 3 steps, i.e., (1) training with the complete portion of the training data set, (2) estimation of the missing attributes with the trained neural networks, and (3) re-training the neural networks with the whole data set. Error back propagation is still applicable to estimate the missing attributes. Unlike other training methods with missing data, it does not assume data distribution models which may not be appropriate for small training data. The developed TEST algorithm is first tested for the Iris benchmark data. By randomly removing some attributes from the complete data set and estimating the values latter, accuracy of the TEST algorithm is demonstrated. Then it is applied to the Diabetes benchmark data, of which about 50% contains missing attributes. Compared with other existing algorithms, the proposed TEST algorithm results in much better recognition accuracy for test data.

title: A filter bank approach to independent component analysis for convolved mixtures
abstract: We present a filter bank approach to perform independent component analysis (ICA) for convolved mixtures. Input signals are split into subband signals and subsampled. A simplified network performs ICA on the subsampled signals, and finally independent components are synthesized. The proposed approach achieves superior performance than the frequency domain approach and faster convergence with less computational complexity than the time domain approach. Furthermore, it requires shorter unmixing filter length and less computational complexity than other filter bank approaches by designing efficient filter banks. Also, a method is proposed to resolve the permutation and scaling problems of the filter bank approach.

title: Out-of-Vocabulary Rejection based on Selective Attention Model
abstract: Based on a psychological selective attention theory a new algorithm is developed to provide reliable out-of-vocabulary (OOV) rejection for speech recognition systems in noisy environments. The developed attention model is based on Broadbent's ‘early filtering’ theory, and the attention adaptation process utilizes a gradient-descent error minimization algorithm with error backpropagation rule. The developed model is applied to isolated-word recognition tasks, and much higher in-vocabulary recognition rates are achieved with the same out-of-vocabulary rejection rates.

title: On the Efficient Speech Feature Extraction Based on Independent Component Analysis
abstract: A new efficient code for speech signals is proposed. To represent speech signals with minimum redundancy we use independent component analysis to adapt features (basis vectors) that efficiently encode the speech signals. The learned basis vectors are sparsely distributed and localized in both time and frequency. Time-frequency analysis of basis vectors shows the property similar with the critical bandwidth of human auditory system. Our results suggest that the obtained codes of speech signals are sparse and biologically plausible.

title: Blind signal separation and independent component analysis
abstract: Independent component analysis (ICA) is emerging as a new paradigm in signal processing and data analysis. ICA has been proposed as a solution to the blind source separation problem (BSS) in which sensory signals are given as mixtures of unknown source signals. Since neither the sources nor the mixing environment are known a priori there is no general analytical solution to this problem. However, since the last decade researchers have found good approximate solutions based on assumptions about the source statistics. They were able to formulate solutions or algorithms that were derived from well-known cost functions including maximum likelihood estimation, information maximization and higher-order cumulants. The ICA framework touches many theoretical research areas, and therefore receives attention in several research communities including machine learning, neural networks, statistical signal processing and Bayesian modeling. More recently, there have been numerous applications of ICA in adaptive 0ltering, speech signal processing, biomedical signal processing, computational neuroscience, image coding, text data modeling, and 0nancial data analysis. The standard ICA formulation has, in general, strong assumptions or simpli0cations such as: the number of sensors has to be equal or greater than the number of sources, there is no additive noise signal in the sensors, and the sources are modeled as random variables. However, when applying ICA to real-world problems it becomes evident that those restrictions are cumbersome and although the standard ICA algorithms give good approximate solutions, there is great need in going beyond the standard assumptions and develop new algorithms that can relax some of the assumptions. This special issue on ICA and BSS is a timely publication covering important recent 0ndings in ICA theory as well as new applications of ICA in data analysis. Leading researchers present their work to advance this 0eld beyond traditional borders and show how this 0eld can bridge disciplines and yield new methods that lead to mutual bene0ts and advances in science and engineering. This issue is composed of a review article and two parts: the 0rst part presents new theoretical 0ndings while the second part presents novel applications. Often, there is no such sharp division into these two parts since illustrative simulations support theoretical 0ndings and practical methods include algorithmic modi0cations to design and evaluate the application-speci0c methods. In “Frontiers of research in BSS=ICA”, V. David S: anchez A. formulates some of the basic concepts and problems in the areas of blind signal separation and independent component analysis. He then moves to a concise description of key methods of data preprocessing and algorithms that provide solutions to the instantaneous mixture and the

title: Feature Sensitive Out-of-Core Chartification of Large Polygonal Meshes
abstract: Mesh chartification is an important tool for processing meshes in various applications. In this paper, we present a novel feature sensitive mesh chartification technique that can handle huge meshes with limited main memory. Our technique adapts the mesh chartification approach using Lloyd-Max quantization to out-of-core processing. While the previous approach updates chartification globally at each iteration of Lloyd-Max quantization, we propose a local update algorithm where only a part of the chartification is processed at a time. By repeating the local updates, we can obtain a chartification of a huge mesh that cannot fit into the main memory. We verify the accuracy of the serialized local updates by comparing the results with the global update approach. We demonstrate that our technique can successfully process huge meshes for applications, such as mesh compression, shape approximation, and remeshing.

title: ARMS: An Authenticated Routing Message in Sensor Networks
abstract: In wireless sensor networks, a sensor node broadcasts its data (such as routing information, beacon messages or meta-data) to all its neighbors, which is called local broadcast. A general case for a sensor node to use a local broadcast is to advertise its routing information. Considering that sensor networks are vulnerable to a variety of attacks and current routing protocols are insecure, a sensor node's broadcast message should be authenticated by all its neighbors. Unfortunately, the previous work on broadcast authentication in sensor networks mainly concentrates on broadcast messages from a base station which has greater capabilities, not from a sensor node. Those schemes' properties are not appropriate for broadcast authentication of sensor node's routing messages. In this paper, we present ARMS, a protocol for broadcast authentication of sensor node's routing messages. It requires only a small memory space, authenticates routing messages without delay (thus, no buffering is needed), needs no time synchronization among sensor nodes, and mitigates the effect of packet loss. These ARMS' properties are suitable for a sensor node to broadcast an authenticated routing message.

title: Embedding of Complete Binary Trees into Meshes with Row-Column Routing
abstract: This paper considers the problem of embedding complete binary trees into meshes using the row-column routing and obtained the following results: a complete binary tree with 2/sup p/-1 nodes can be embedded (1) with link congestion one into a /sup 9///sub 8//spl radic/(2/sup p/)/spl times//sup 9///sub 8//spl radic/(2/sup p/) mesh when p is even and a /spl radic/(/sup 9///sub 8/2/sup p/)/spl times//spl radic/(/sup 9///sub 8/2/sup p/) mesh when p is odd, and (2) with link congestion two into a /spl radic/(2/sup p/)/spl times//spl radic/(2/sup p/) mesh when p is even, and a /spl radic/(2/sup p-1/)/spl times//spl radic/(2/sup p-1/) mesh when p is odd.

title: Noise-robust automatic speech recognition using mainlobe-resilient time-frequency quantile-based noise estimation
abstract: In standard speech recognition systems in which training data are clean speech, the presence of background noise in received signal can severely deteriorate the recognition performance. This paper presents a simple noise-robust speech recognition system based on a modified noise spectral estimation method called mainlobe-resilient time-frequency quantile-based noise estimation (M-R T-F QBNE), which focuses on the mainlobes at harmonic frequencies. We estimate the global signal-to-noise ratio (SNR) and select a recognition model, which is best matched to the SNR operating range. Experimental results show that the recognition accuracy of the proposed recognition system is higher than that of the AURORA2 clean training baseline by 23%. Compared to multicondition training, the proposed method achieves comparable recognition accuracy.

title: Low-power network-on-chip for high-performance SoC design
abstract: An energy-efficient network-on-chip (NoC) is presented for possible application to high-performance system-on-chip (SoC) design. It incorporates heterogeneous intellectual properties (IPs) such as multiple RISCs and SRAMs, a reconfigurable logic array, an off-chip gateway, and a 1.6-GHz phase-locked loop (PLL). Its hierarchically-star-connected on-chip network provides the integrated IPs, which operate at different clock frequencies, with packet-switched serial-communication infrastructure. Various low-power techniques such as low-swing signaling, partially activated crossbar, serial link coding, and clock frequency scaling are devised, and applied to achieve the power-efficient on-chip communications. The 5 /spl times/5 mm/sup 2/ chip containing all the above features is fabricated by 0.18-/spl mu/m CMOS process and successfully measured and demonstrated on a system evaluation board where multimedia applications run. The fabricated chip can deliver 11.2-GB/s aggregated bandwidth at 1.6-GHz signaling frequency. The chip consumes 160 mW and the on-chip network dissipates less than 51 mW.