waashk/dblp huggingface dataset:
approaching user capacity in a dsl system via harmonic mean-rate optimization. in this paper we consider a digital subscriber line (dsl) system with n orthogonal narrowband tones. each user has a limited power budget, and our goal is to determine the power allocation of each user that enables the ‘user capacity’ of the system to be approached. in this paper, we use ‘user capacity’ to denote the maximum number of users that can be supported by the system, provided that each user is guaranteed to have a data rate that lies within a prescribed range. finding a power allocation that enables this capacity to be approached directly can be quite cumbersome because it involves solving a (non-convex) integer-program. in order to circumvent this difficulty, in this paper we propose an alternate approach that is based on exploiting the fairness and per-tone convexity of the harmonic mean-rate objective. using these features, we devise a computationally-efficient power allocation technique that enables the user capacity of the dsl system to be approached more closely than power allocation techniques that are more computationally demanding.
reversible data hiding in highly efficient compression scheme. nowadays, most multimedia is stored in compressed bit stream format to save the storage apace or transmission time. this study proposes a novel technique for embedding flexible amounts of data in the bitmap of the improved ordered dither block truncation coding (odbtc) image, where the ordered dithering is used to dither the quantized btc image to avoid the annoying false contour and blocking effect inherently existed in btc image. moreover, the lut strategy is also used to significantly reduce the complexity. the inverse halftoning and the second round of halftoning are employed as the key steps in locating the embedded information bits. experimental results demonstrate that an objective good quality image with flexible capacity and reasonable complexity is obtained. moreover, the correct decoding rate of 100% is maintained, and the original host odbtc image can also be reconstructed in the decoder when needed, which significantly boosts the flexibility in image quality control.
video quality monitoring of streamed videos. this paper describes a video quality analysis system for inservice monitoring of streamed videos, particularly over mobile/wireless networks. the algorithm adopts the no-reference method, and enables real-time measurement of video quality at any point in the content production and delivery chain using any given video. the technologies developed include no-reference methods for measuring picture freeze, picture loss, and blockiness. the developed system (where the software has not been optimized for speed) is able to process video of cif size (352×288 pixels) at more than 30 fps on a pentium-iv 3ghz computer. the experimental results show that the proposed video quality analysis system gives good accuracy for picture freeze, picture loss, and blocking detections.
wheezing sounds detection using multivariate generalized gaussian distributions. a wheeze is a continuous, coarse, whistling sound produced in the respiratory airways during breathing, commonly experienced by persons suffering from asthma. in this paper, we present a new method for the detection of wheezing sounds in the normal breathing sounds. in our study we perform an accurate statistical analysis of breathing signals. we suggest a modeling for wheezing and normal sounds in the wavelet packet domain using generalized gaussian distributions. our detection method is based on a specific multimodal markovian modeling proposed in a bayesian framework. we cope with the multidimensional aspect of the generalized gaussian distribution by using the theory of copulas. experimental results are given in detail in this paper.
an effective flowestimation method with particle filter based on helmholtz decomposition theorem. this paper proposes a novel flow estimation method with a particle filter based on a helmholtz decomposition theorem. the proposed method extends a model of the helmholtz decomposition theorem and enables the decomposition of flows into rotational, divergent, and translational components. from the extended model, the proposed method defines a state transition model and an observation model of the particle filter. furthermore, the proposed method derives an observation density of the particle filter from an energy function based on the helmholtz decomposition theorem. by utilizing these novel approaches, the proposed method provides a solution to the problem in the traditional ones of not being able to realize an effective flow estimation with the particle filter based on rotation, divergence, and translation, which are important geometric features. consequently, the proposed method can accurately estimate the flows.
robust video fingerprinting based on visual attention regions. this paper presents a robust video fingerprinting based on visual attention regions. video fingerprints, which are a set of short feature vectors, are unique to video clips and used for video identification. the performance of video fingerprinting is usually measured in terms of robustness and accuracy of identification. in our proposed approach, we extract video fingerprints using visual attention regions which remain the same for the perceptually same scenes with different types of distortions and different for different scenes. the experimental results show that the proposed video fingerprinting is effective for constructing video fingerprints that are robust against various content-preserving distortions and accurate in identifying different video clips.
robust sampling and reconstruction methods for compressed sensing. recent results in compressed sensing show that a sparse or compressible signal can be reconstructed from a few incoherent measurements. compressive sensing systems are not immune to noise, which is always present in practical acquisition systems. in this paper we propose robust methods for sampling and reconstructing sparse signals in the presence of impulsive noise. analysis of the proposed methods demonstrates their robustness under heavy-tailed models. simulations show that the proposed methods outperform existing compressed sensing techniques in impulsive environments, while having similar performance in light-tailed environments.
symmetric distributed multiview video coding. symmetric distributed source coding has made great progress in the past several years. however, few attempts have been tried to use the symmetric distributed source coding schemes in real data compression scenarios. in this paper, with the inspiration of previous symmetric distributed source coding schemes, we first put forward a novel syndrome-based symmetric distributed coding scheme which can achieve the whole slepian-wolf rate region. then we propose a general architecture for symmetric distributed multiview video schemes. our experiments show very encouraging results. in the high-rate case, our scheme can achieve great rate saving from separate h.264 coding scheme at the same video quality.
vectorized deblocking filter for hd h.264 decoding on cell/b.e. for high definition (hd) h.264 decoding, deblocking filter is one of the most time-consuming modules. this paper proposes several vectorization approaches to speed up it on a single synergistic processor element (spe) of ibm cell broadband engine (cell/b.e.) processor, by which great performance improvements are achieved. the average deblocking speed is 42.4 frames per second (fps) for 141 1080p h.264 video streams. the steady-going performance is obtained for different streams of various contents and bitrates. with the vectorized deblocking filter, the new hd h.264 decoder is able to decode two 1080p h.264 video streams simultaneously on playstation® 3 (ps3) in real-time. the proposed approaches are practical on many other vector processor platforms, but not limited to cell/b.e. processor.
from rule-based to statistical grammars: continuous improvement of large-scale spoken dialog systems. statistical spoken language understanding grammars (sslus) are often used only at the top recognition contexts of modern large-scale spoken dialog systems. we propose to use sslus at every recognition context in a dialog system, effectively replacing conventional, manually written grammars. furthermore, we present a methodology of continuous improvement in which data are collected at every recognition context over an entire dialog system. these data are then used to automatically generate updated context-specific sslus at regular intervals and, in so doing, continually improve system performance over time. we have found that sslus significantly and consistently outperform even the most carefully designed rule-based grammars in a wide range of contexts in a corpus of over two million utterances collected for a complex call-routing and troubleshooting dialog system.
inventory based speech enhancement for speaker dedicated speech communication systems. we are presenting a method for the enhancement of speech in speaker dedicated speech communication systems. the proposed procedure is fundamentally different from most state-of-the-art filtering approaches. instead of filtering a distorted signal we are re-synthesizing a new “clean” signal based on its likely characteristics. these characteristics are estimated from the distorted signal. we present a successful implementation of the proposed method for a communication system for which speaker enrollment and noise enrollment are feasible. forty minutes of clean speech training data is usually sufficient for successful denoising. the proposed method compares very favorably to other state-of-the-art systems in both objective and subjective speech quality assessments.
linear predictive modelling of gait patterns. the use of a wearable triaxial accelerometer for unsupervised monitoring of human movement has become a major research focus in recent years. in this paper, the relationship between accelerometry signals and human gait is analysed using a linear prediction (lp) model. we explore the use of the lp model for analysing five gait patterns and show that the lp cepstrum can be used for gait pattern classification with high accuracy. this is then compared to a filterbank based approach to estimate the cepstral coefficients. fifty subjects participated in collection of gait pattern data involving walking on level surfaces, and walking up and down stairs and ramps. the results show that an overall accuracy of 93% can be achieved using features derived from the cepstral coefficients for the five different walking patterns.
multi-modal activity and dominance detection in smart meeting rooms. in this paper a new approach for activity and dominance modeling in meetings is presented. for this purpose low level acoustic and visual features are extracted from audio and video capture devices. hidden markov models (hmm) are used for the segmentation and classification of activity levels for each participant. additionally, more semantic features are applied in a two-layer hmm approach. the experiments show that the acoustic feature is the most important one. the early fusion of acoustic and globalmotion features achieves nearly as good results as the acoustic feature alone. all the other early fusion approaches are outperformed by the acoustic feature. more over, the two-layer model could not achieve the results of the acoustic features.
a low complexity channel decomposition and feedback strategy for mimo precoder design. in classical mimo systems, singular value decomposition (svd) is adopted as a common way to decompose the mimo channel into parallel subchannels. however, in addition to its high complexity, it is also sensitive to the ill-conditioning of the channel matrix. in this paper, we propose a channel decomposition strategy called ldlh decomposition for low complexity mimo precoder design. we show that the computation complexity of ldlh achieves one degree of magnitude lower than that of svd, as well as some svd-based decomposition scheme such as geometric mean decomposition (gmd) and uniform channel decomposition (ucd). in addition, we also show that ldlh-based precoder requires less quantization effort and feedback bandwidth.
on the side-information dependency of the temporal correlation in wyner-ziv video coding. current models in wyner-ziv video coding consider the temporal correlation noise to be side-information independent (sii). this paper goes beyond this assumption and proposes a novel model, of which the parameters are side-information dependent (sid). the proposed model is experimentally validated showing remarkable accuracy improvement over the conventional sii model. moreover, a novel sid technique for the accurate estimation of the correlation channel in video is introduced. the proposed technique enables the design of a novel pixel-domain wyner-ziv video coding system operating without a feedback channel. preliminary experimental results show that the proposed codec achieves superior performance compared to the state-of-the-art in pixel-domain wyner-ziv coding.
contrasting emotion-bearing laughter types in multiparticipant vocal activity detection for meetings. the detection of laughter in conversational interaction presents an important challenge in meeting understanding, important primarily because laughter is predictive of the emotional state of participants. we present evidence which suggests that ignoring unvoiced laughter improves the prediction of emotional involvement in collocated speech, making a case for the distinction between voiced and unvoiced laughter during laughter detection. our experiments show that the exclusion of unvoiced laughter during laughter model training as well as its explicit modeling lead to detection scores for voiced laughter which are much higher than those otherwise obtained for all laughter. furthermore, duration modeling is shown to be a more effective means of improving precision than interaction modeling through joint-participant decoding. taken together, the final detection f-scores we present for voiced laughter on our development set comprise a 20% reduction of error, relative to f-scores for all laughter reported in previous work, and 6% and 22% relative reductions in error on two larger datasets unseen during development.
robust-sl0 for stable sparse representation in noisy settings. in the last few years, we have witnessed an explosion in applications of sparse representation, the majority of which share the need for finding sparse solutions of underdetermined systems of linear equations (usle's). based on recently proposed smoothed ℓ0-norm (sl0), we develop a noise-tolerant algorithm for sparse representation, namely robust-sl0, enjoying the same computational advantages of sl0, while demonstrating remarkable robustness against noise. the proposed algorithm is developed by adopting the corresponding optimization problem for noisy settings, followed by theoretically-justified approximation to reduce the complexity. stability properties of robust-sl0 are rigorously analyzed, both analytically and experimentally, revealing a remarkable improvement in performance over sl0 and other competing algorithms, in the presence of noise.
a parafac-based technique for detection and localization of multiple targets in a mimo radar system. in this paper, we show that the problem of detection and localization of multiple targets in a bistatic mimo radar system can be solved by parallel factor (parafac) analysis. our method is deterministic and fully capitalizes on the strong algebraic structure of the received data, where the radar cross section (rcs) fluctuation is not regarded as a nuisance parameter but rather as a source of time diversity. simulation results show that our technique outperforms existing beamforming-based radar imaging methods at a lower complexity.
a specific qrs detector for electrocardiography during mri: using wavelets and local regularity characterization. automatic electrocardiogram (ecg) analysis, especially qrs detection, is still a challenging task. this is even more the case when ecg is acquired during magnetic resonance (mr) examination. the mr environment highly distorts ecg, with hall effect, due to the important static magnetic field, and artifacts, caused by fast switching magnetic field gradients. detection of qrs complexes is then affected. in this paper, a new specific mr qrs detector is presented. this method is based on the modulus maximum lines and on the lipschitz exponent estimation they offer. the use of this regularity characterization enables to distinguish between qrs complexes and mr artifacts. this detector outperforms existing algorithms with almost 99% sensitivity and positive prediction value.
structuring and analyzing low-quality lecture videos. this paper presents a lecture video structuring and analysis scheme to provide students an efficient way to access the lecture content. instead of using color-based or histogram-based methodologies, we propose a new edge-based shot boundary detection algorithm to accurately rebuild the slide structure. the proposed approach can successfully resist the unwanted influences induced from the variant illumination condition and occlusions. besides, original slide content can be extracted excluding any obstruction by using human removing techniques. furthermore, the teaching focus is analyzed so that this system becomes more useful for learning.
improving the performance of vtln under mismatched speaker conditions and making it approach that of matched speaker conditions. the performance of conventional vtln for mis-matched train and test speaker conditions (e.g. adult-train child-test) does not approach the performance of matched speaker conditions (e.g. child-train child-test). in this paper, we investigate this problem and propose methods to reduce this gap in performance. we use our recently proposed linear transformation approach to vtln, that also enables us to study the effect of jacobian unlike conventional vtln. the main advantage of transform-based vtln over adaptation based approaches (like cmllr), is that it does not require any matrix estimation. we argue that the degraded vtln performance under mismatched speaker conditions is due to the significant frequency warping that is necessary for normalization which leads to a mis-match between the correlation in the feature components of the test data and the covariance structure of the trained/normalized model. we show that the use of a global de-correlating transform (mllt) leads to improved vtln performance. we finally show that using both jacobian and mllt together improves the vtln performance for mis-matched cases with the performance approaching that of matched speaker conditions.
instantaneous frequency rate estimation for high-order polynomial-phase signal. for a high-order polynomial-phase signal (pps), instantaneous frequency rate (ifr), which is defined as the second derivative of the phase, is estimated by using an estimator with only a second-order nonlinearity. compared to high-order phase function (hpf), the proposed ifr estimator presents improved performance including smaller mean-squared error (mse) and lower snr threshold. statistical analysis via a multivariate first-order perturbation analysis is derived for the estimate bias and mse. numerical results verify our analytical results.
optimizing segment label boundaries for statistical speech synthesis. this paper introduces a new optimization technique for moving segment labels (phone and subphonetic) to optimize statistical parametric speech synthesis models. the choice of objective measures is investigated thoroughly and listening tests show the results to significantly improve the quality of the generated speech equivalent to increasing the database size by 3 fold.
multi-level non-rigid image registration using graph-cuts. non-rigid image registration is widely used in medical image analysis and image processing. it remains a challenging research problem due to its smoothness requirement and high degree of freedoms in the deformation process. in [1], a method is proposed to solve non-rigid image registration via graph-cuts algorithm by modeling the registration process as a discrete labeling problem. a displacement label (vector) is assigned to each pixel in the source image to indicate the corresponding position in the floating image. the whole system is then optimized by using the graph-cuts algorithm via alpha-expansions [2]. as the initial point is not required for the graph-cuts algorithm, the method proposed by [1] is a single-level registration. in this paper, rather single-level, we enable multi-level non-rigid image registration using graph-cuts by passing the deformation field of the current resolution level to the successive finer one. by applying the proposed multi-level registration method, the number of labels used in each level is greatly reduced due to lower image resolution being used in coarser levels. therefore, the speed of the registration process is improved. we compare our results with the original single-level version, demons and ffd. it is found that our method improves the speed of non-rigid image registration by 50% and meanwhile maintains similar robustness and registration accuracy with the single-level version.
2d sound source mapping from mobile robot using beamforming and particle filtering. this paper describes a particle filter based sound source mapping system that builds 2d sound source maps from directional sound readings taken from a mobile robot. the method uses a sound source localization model that is represented by gaussian distribution for both direction and distance. to do this, accurate directional localization of sound sources is required, and two key components have been developed to achieve this: 1) a 32ch low side-lobe microphone array that is designed by beam forming simulation to have a) an omni-directional response, b) a narrower main-lobe, and c) lower side-lobes, in 700–2500[hz] acoustic signals; 2) directional localization of different pressure sound sources by combining the delay and sum beam forming (dsbf) and the frequency band selection (fbs) methods. finally, experimental results show the proposed method can map sound sources in two dimesionals with high accuracy (less than 50[cm] error).
cooperative mimo for alien noise cancellation in upstream vdsl. we present cooperative mimo for alien noise cancellation (comac): a per-tone, blind, low-complexity, linear, and adaptive noise whitening algorithm for alien crosstalk mitigation in upstream vectored vdsl systems. comac directly acts on the residual errors of the vectored users after self-fext cancellation and frequency domain equalization, and thus, leverages the inherent alien-crosstalk-induced spatial correlation across users. comac employs a lowcomplexity recursion scheme derived from the optimal mmse noise whitener to non-disruptively initialize, engage, and adapt the noise canceller while the vectored users operate in data mode. assuming reliable transmit symbol estimation at its input, we show that comac achieves the cramer-rao lower bound. further, the snr improvements accruing from comac can be translated into substantial rate improvements for upstream vectored vdsl.