waashk/dblp huggingface dataset:
neighborhood gossip: concurrent averaging through local interference. in this paper, we study a gossip algorithm for distributed averaging over a wireless sensor network. the usual assumption is that, through properly chosen codes, the physical layer is reduced to a set of reliable bit pipes for the distributed averaging algorithm. however, with a new channel coding technique, computation coding, we can exploit the interference property of the wireless medium for efficient averaging. this then provides a new abstraction for the physical layer: reliable linear equations instead of reliable bit pipes. the “neighborhood gossip” algorithm operates modularly on top of this abstraction. we will show that for certain regimes, such an approach can lead to energy savings that are exponential in the network size and time savings that are polynomial.
a performance-weighted mixture of lms filters. in this paper, we explore the use of a particular multistage adaptation algorithm for a variety of adaptive filtering applications where the structure of the underlying process to be estimated is unknown. the proposed algorithm uses a performance-weighted mixture of lms filters of various orders to construct its final output. the algorithm is analyzed in a stochastic context with respect to its convergence and mean-square error (mse) behaviors and is shown to achieve the best mse performance of the constituent algorithms in the mixture. through simulations, it has been observed that the mixture structure can offer considerable performance improvement for both stationary and time varying observation sequences.
ofdm turbo decodulation with exit optimized bit loading and signal constellations. we propose the combination of orthogonal frequency division multiplexing (ofdm) and turbo decodulation (tdec) - a multiple turbo process consisting of iterative demodulation and iterative source-channel decoding - for transmission of correlated source codec parameters over wireless frequency-selective broadband fading channels. as ofdm systems split up frequency-selective fading channels into orthogonal flat-fading channels, individual modulation signal constellations sets with different bit mapping rules can be assigned to subcarriers taking into account the iterative reception and decoding process. the superior performance of the proposed ofdm-tdec system is demonstrated by analyzing the achieved residual bit error rate (ber) and parameter signal-to-noise ratio (snr).
adaptive newton algorithms for blind equalization using the generalized constant modulus criterion. two newton-type algorithms using the generalized complex modulus (gcm) criterion for blind equalization and carrier phase recovery are proposed. first the partial hessian and full hessian of the real gcm loss function with complex valued arguments are calculated by second-order differential. then an adaptive pseudo newton learning algorithm and a full newton learning algorithm are designed. by using the matrix inversion lemma, both newton algorithms can be implemented with a computational complexity of o(l2) efficiently, where l is the length of equalizer. simulation results demonstrate that the two newton algorithms can achieve automatic carrier phase recovery and exhibit fast convergence rates.
optimal network beamforming for bi-directional relay networks. we consider a relay network which consists of two transceivers and r relay nodes. we study a half-duplex two-way relaying scheme. first, the two transceivers transmit their information symbols simultaneously and the relays receive a noisy mixture of the two transceiver signals. then each relay adjusts the phase and the amplitude of its received signal by multiplying it with a complex beamforming coefficient and transmits the so-obtained signal. aiming at optimally calculating the beamforming weight vector as well as the transceiver transmit powers, we minimize the total transmit power subject to two constraints on the receive signal-to-noise ratios (snrs) at the two transceivers. we show that the optimal weight vector can be obtained through a simple iterative algorithm which enjoys a linear computational complexity per iteration.
fast gender recognition by using a shared-integral-image approach. we develop a new approach for gender recognition. in this paper, our approach uses the rectangle feature vector (rfv) as a representation to identify humans' gender from their faces. the rfv is computationally fast and effective to encode intensity variations of local regions of human face. by only using few rectangle features learned by adaboost, we present a gender identifier. we then use nonlinear support vector machines for classification, and obtain more accurate identification results.
cultural style based music classification of audio signals. music classification based on cultural style is useful for music analysis and has potential applications in retrieval and recommendation systems. in this paper, we present the first attempt to classify audio signals automatically according to their cultural styles, which are characterized by timbre, rhythm, wavelet coefficients and musicology-based features. machine learning algorithms are employed to investigate the effectiveness of various features on a data set of 1300 music pieces. experimental results show that the proposed method can achieve an overall accuracy of 86% for six cultural styles, which shows the feasibility of integrating cultural style classification into music retrieval systems.
support vector machines and joint factor analysis for speaker verification. this article presents several techniques to combine between support vector machines (svm) and joint factor analysis (jfa) model for speaker verification. in this combination, the svms are applied to different sources of information produced by the jfa. these informations are the gaussian mixture model supervectors and speakers and common factors. we found that using svm in jfa factors gave the best results especially when within class covariance normalization method is applied in order to compensate for the channel effect. the new combination results are comparable to other classical jfa scoring techniques.
bayesian and hybrid cramer-rao bounds for qam dynamical phase estimation. in this paper, we study bayesian and hybrid cramer-rao bounds for the dynamical phase estimation of qam modulated signals. we present the analytical expressions for the various crbs. this avoids the calculation of any matrix inversion and thus greatly reduces the computation complexity. through simulations, we also illustrate the behaviors of the bcrb and of the hcrb with the signal-to-noise ratio.
openmp-based parallel implementation of a continuous speech recognizer on a multi-core system. we have implemented a 20,000-word continuous speech recognizer on a multi-core based system. a fine grain parallel processing approach is employed for good scalability, and the openmp library is used for enhanced portability. in the emission probability computation, a dynamic workload distribution method is employed for good load balancing. however, the search network involved in the viterbi beam search is statically partitioned into independent subtrees to reduce memory synchronization overhead. in order to further improve the performance, a workload predictive thread assignment strategy as well as a false cache line sharing prevention method are employed. the test was conducted using wsj1 20k test and development set. we achieved the speed-up of 3.90 by utilizing four threads parallelization in a four-core system compared to four copies of the baseline single thread speech recognizer running simultaneously. the final recognition system runs about twice the speed of the real-time requirement.
interpolation error as a quality metric for stereo: robust, or not? to properly benchmark and stimulate current stereo algorithms specifically in the application context of view interpolation, a robust quantitative evaluation approach is important. as a prevailing quality assessment method, interpolation error has been widely used. it measures the distortions between an interpolated image and a real camera image for a desired virtual viewpoint. however, is it a robust quality metric, especially when state-of-the-art stereo technology is developing so fast? this paper hence focuses on revealing several rarely attended weaknesses that make the interpolation error evaluation paradigm vulnerable. in addition, we propose an alternative evaluation method as an early attempt at addressing these challenges, from a perspective of communication system. evaluation of representative stereo methods from the middlebury website shows that the new approach yields consistent quality assessment outcomes.
event recognition with time varying hidden markov model. standard hidden markov model (hmm) and the more general dynamic bayesian network (dbn) models assume stationarity of state transition distribution. however, this assumption does not hold for many real life events of interest. in this paper, we propose a new time sequence model that extends hmm to time varying scenario. the time varying property is realized in our model by explicitly allowing the change of state transition density as the time spent in a particular state passes by. rather than keeping transition densities at different time spots independent of each other, we exploit their temporal correlation by applying a hierarchical dirichlet prior. this leads to a more robust time varying model, especially when training data are scarce. we also employ markov chain monte carlo (mcmc) sampling in learning the map estimate of time varying parameters, with a transition kernel incorporating linear optimization. the proposed model is applied to recognizing real video events, and is shown to outperform existing hmm-based methods.
multiple ica-based real-time blind source extraction applied to handy size microphone. a new blind source extraction method in widespread noise conditions is proposed, which is based on multiple frequency-domain independent component analysis (fdica) combining projection back and spectral subtraction. in addition, we implement the proposed method to digital signal processor (dsp) for a more realistic real-time operation, and develop a new blind source extraction (bse) microphone which can extract a target sound in real-time. in this paper, we illustrate and evaluate the proposed method and bse microphone. and experimental results reveal that the extraction performance of the proposed method are superior to that of conventional methods, and we show the efficacy of microphone.
language model transformation applied to lightly supervised training of acoustic model for congress meetings. for effective training of acoustic and language models for spontaneous speech such as meetings, it is significant to exploit the texts available in a large scale, which may not be faithful transcripts of the utterances. we have proposed a language model transformation scheme to cope with the differences between verbatim transcripts of spontaneous utterances and human-made transcripts such as those in proceedings. in this paper, we investigate its application to lightly supervised training of the acoustic model. by transforming the corresponding text in the proceedings, we can generate a very constrained model to predict the actual utterances. the experimental evaluation with the transcription system for the japanese congress meetings demonstrated that the proposed scheme can generate accurate labels for acoustic model training and thus realizes the comparable asr (automatic speech recognition) performance to the case using manual transcripts.
scalable distributed source coding. this paper considers the problem of scalable distributed coding of correlated sources that are communicated to a central unit. the general setting is typically encountered in sensor networks. the conditions of communication channels between the source encoders and fusion center may be time-varying and it is often desirable to guarantee a base layer of coarse information during channel fades. in addition the desired system should be robust to various scenarios of channel failure and should utilize all the available information to attain the best possible compression efficiency. although a standard ‘lloyd-style’ distributed coder design algorithm can be generalized to scalable distributed source coding, the resulting algorithm depends heavily on initialization and will virtually always converge to a poor local minimum on the distortion-cost surface. we propose an efficient initialization scheme for such a system, which employs a properly designed multi-stage distributed coder. in our prior work, we highlighted the fundamental conflict that arises when multi-stage coding is directly combined with distributed quantization. here we use the multi-stage distributed coding system to initialize a scalable distributed coder and propose an iterative algorithm for joint design of all system components once the structural constraint is removed. simulation results show considerable gains over randomly initialized scalable distributed coder design.
morphing of transient sounds based on shift-invariant discrete wavelet transform and singular value decomposition. in this paper, a new morphing algorithm for transient sounds is introduced. input sounds are first projected onto orthogonal bases from which intermediate or novel sounds can be generated. the proposed algorithm uses a shift invariant version of discrete wavelet transform and the singular value decomposition (svd) to represent the input sound signals over a set of orthogonal bases. interpolation is carried out between the weight vectors from the svd to produce a new weight vector used for synthesising a new set of wavelet coefficients. the morphed sound is generated by taking the inverse discrete wavelet transform of the combined weighted bases. the proposed algorithm not only generates a range of new sounds, but also represents the input sounds in a more compact fashion.
performance bounds of mimo receivers in the presence of radio frequency interference. multi-input multi-output (mimo) receivers have generally been designed and their communication performance analyzed under the assumption of additive gaussian noise. wireless transceivers, however, may also be affected by radio frequency interference (rfi) that is well modeled using non-gaussian impulsive statistics. in this paper, we derive bounds on the communication performance for a two transmit, two receive antenna mimo system in the presence of rfi. our contributions include derivation of (1) channel capacity in the presence of rfi, (2) probability of symbol error for uncoded transmissions, and (3) chernoff bound on the pairwise error probability and cutoff rate as a measure of the throughput performance for coded transmissions. comparison with the communication performance bounds for receivers designed assuming additive gaussian noise demonstrates degradation in communication performance in the presence of rfi.
cadence analysis of temporal gait patterns for seismic discrimination between human and quadruped footsteps. this paper reports on a method of cadence analysis for the discrimination between human and quadruped using a cheap seismic sensor. previous works in the domain of seismic detection of human vs. quadruped have relied on the fundamental gait frequency. slow movement of quadrupeds can generate the same fundamental gait frequency as human footsteps therefore causing the recognizer to be confused when quadruped are ambling around the sensor. here we propose utilizing the cadence analysis of temporal gait pattern which provides information on temporal distribution of the gait beats. we also propose a robust method of extracting temporal gait patterns. features extracted from gait patterns are modeled with optimum number of gaussian mixture models (gmms). the performance of the system during the test for discriminating between horse, dog, multiple people walk, and single human walk/run was over 95%.
rate distribution between model and signal for multiple descriptions. we consider the rate allocation problem for multiple-description quantization of the signal described by an adaptive model with a fixed structure. the source modeling in coding generally results in a two-stage description of the data, where one of the stages describes the model parameters, and the other describes the signal. such a setup implies the existence of a trade-off between the rate spent on the parameters and the rate spent on the signal. we optimize this trade-off analytically for the multiple-description case using a method inspired by minimum description length principle. we also provide an algorithm for optimizing the rate allocation between the components of the model-based multiple description coder. finally we experimentally confirm our results. our method facilitates the rate-adaptive multiple-description coding.
a new method to find an optimal warping function in image stitching. in image stitching applications, it is very important to find a suitable warping function for the visual quality of a composite (stitching result). in this paper, we present a new mathematical criterion to select an optimal warping function among a set of possible candidates (e.g., parametric family). the proposed criterion can be considered as a direct view condition for image stitching , i.e., it is desirable that each part of the composite image looks like its corresponding input image. more specifically, we do not use an explicit modeling of a compositing surface, but, we focus on the differential properties of a warping function. that is we design a cost function so that the jacobian matrix of a warping function is close to a shape preserving matrix such as rotation and reflection matrices. the proposed cost function can be effectively minimized by using levenberg-marquardt algorithm. the experimental results show that the proposed method results in visually pleasing stitched results because the original shape of each image is preserved in the composite.
revisiting quantization theorem through audiowatermarking. in this paper, we propose the concept of “doping watermarking”, whose principle is to add an imperceptible noise to an host signal in order to improve its properties. especially, our aim is to reduce the spectral support of the probability density function (pdf) of an audio signal in order to match the conditions of the quantization theorem. in this context, we develop a specific audiowatermarking algorithm and test its performance on real audio signals. this watermark allows to recover the pdf of a digital signal from a sub-quantized version of the signal, with very low error.
nonparametric curve alignment. congealing is a flexible nonparametric data-driven framework for the joint alignment of data. it has been successfully applied to the joint alignment of binary images of digits, binary images of object silhouettes, grayscale mri images, color images of cars and faces, and 3d brain volumes. this research enhances congealing to practically and effectively apply it to curve data. we develop a parameterized set of nonlinear transformations that allow us to apply congealing to this type of data. we present positive results on aligning synthetic and real curve data sets and conclude with a discussion on extending this work to simultaneous alignment and clustering.
multi-foveation filtering. we present a method for computing a function of average multi-viewer eye sensitivity based on the geisler & perry contrast threshold formula, and, from this, the cut-off frequency map (as used in foveation filtering) that is optimal in the sense of discarding frequencies in least-noticeable-first order. existing approaches usually solve the multi-viewer foveation problem as a number of single-viewer foveations, effectively taking collective sensitivity to be the maximum of the individual viewer eye sensitivities. this has inherent problems such as over-sensitivity to outliers which are not problems with the proposed approach. furthermore, the proposed approach can be employed in the infinite-viewer (probability-based) scenario without additional cost.
unimodular sequence design for good autocorrelation properties. unimodular (i.e., constant modulus) sequences with good autocorrelation properties are useful in several areas, including communications, radar and sonar. the integrated sidelobe level (isl) is often used to express the goodness of the autocorrelation properties of a given sequence. in this paper, we present several cyclic algorithms for the local minimization of isl-related metrics. to illustrate the performance of the proposed algorithms, we present a number of examples including the design of sequences that have virtually zero autocorrelation sidelobes in a specified lag interval, and of long sequences that could hardly be handled by means of other algorithms previously suggested in the literature.
volterra series for analyzing mlp based phoneme posterior estimator. we present a framework to apply volterra series to analyze multi-layered perceptrons trained to estimate the posterior probabilities of phonemes in automatic speech recognition. the identified volterra kernels reveal the spectro-temporal patterns that are learned by the trained system for each phoneme. to demonstrate the applicability of volterra series, we analyze a multilayered perceptron trained using mel filter bank energy features and analyze its first order volterra kernels.
a frequency hopping spread spectrum transmission scheme for uncoordinated cognitive radios. one of the major challenges to cognitive radios is the synchronization of distributed radios onto the same spectrum white spaces which vary in time and space. in this paper, we propose a frequency-hopping spread spectrum transmission scheme which works reliably without any a priori handshaking assumption. each cognitive radio independently detects white spaces, and then selects one of them to transmit or receive signals according to a pre-defined frequency hopping pattern. while exploiting the reliability of the white space detection capability of cognitive radios, the new scheme is robust to even large detection errors. according to the accuracy of the spectrum sensing, both the secondary data rate and the interference to primary users can be optimized by adjusting the spreading gain. its performance is analyzed and demonstrated by simulations.
a new approach for the reassignment of time-frequency representations. the reassignment method is a widespread approach for obtaining high resolution time-frequency representations. nevertheless, its performance is not always optimal and can deteriorate for low signal-to-noise ratio (snr) values. in order to overcome these obstacles, a novel method for obtaining high resolution time-frequency representations is proposed in this paper. the new method implements recently proposed nonparametric snakes in order to obtain accurate locations of the signal ridges in the time-frequency domain. the results of numerical analysis show that the proposed method is capable of achieving significantly higher concentration of signals in the time-frequency domain in comparison to the spectrogram and the traditional reassignment method. furthermore, the new scheme also maintains good performance for low snr values, while the performance of the other two considered methods significantly diminishes. it is clear from the results that the proposed method might be of significance in applications where accurate estimation of the signal components is required for low snr values.
a game theoretical algorithm for joint power and topology control in distributed wsn. in this paper, the issue of network topology control in wireless networks using a fully distributed algorithm is considered. whereas the proposed distributed algorithm is designed applying game theory concepts to design a non-cooperative game, network connectivity is guaranteed based on asymptotic results of network connectivity. simulations show that for a relatively low node density, the probability that the proposed algorithm leads to a connected network is close to one.