waashk/dblp huggingface dataset:
robust bayesian tracking on riemannian manifolds via fragments-based representation. recently, the covariance region descriptor [1] has been proved robust and versatile for a modest computational cost. it enables efficient fusion of different types of features. based on the covariance descriptor and the metric on riemannian manifolds, we develop a robust bayesian tracking framework via fragments-based representation in this paper. in this framework, the template object is represented by multiple image fragments or patches. every patch votes on the possible state of the object in the current frame, by comparing its covariance descriptor with the corresponding image patch model. tracking is then led by the bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. the weight of each particle is formulated by combining the votes of the patches using a robust statistic. further, we extend the fast covariance computation to the bayesian tracking problem, which makes the tracking procedure more efficient. we present extensive experimental results on challenging sequences, which demonstrate the robust tracking achieved by our algorithm.
design of critically subsampled dft filter-banks with allpass polyphase filters and near-perfect reconstruction. a new design for a recursive dft analysis-synthesis filter-bank (as fb) with critical subsampling and near-perfect reconstruction is proposed. the analysis filter-bank consists of allpass polyphase filters to achieve a high frequency selectivity with a low algorithmic complexity and low signal delay. the condition for perfect reconstruction (pr) leads to either unstable or anti-causal synthesis filters. this problem is solved by stable allpass polyphase synthesis filters which are designed by analytical closed-form expressions. the first design can achieve arbitrarily small aliasing, amplitude and phase distortions in dependence of the tolerable signal delay and algorithmic complexity. the second design avoids aliasing and amplitude distortions and minimizes the phase distortions at the expense of an increased system delay. the proposed iir filter-bank possesses also a significantly lower algorithmic complexity than comparable fir filter-banks.
a novel robust kernel for applications to images. robustness is an essential issue to computer vision and pattern recognition in developing multimedia applications. in this work, we present a robust kernel approach that is highly robust against random noises and intra-class deformations. by incorporating the robust error function used in robust statistics together with a deformation-invariant distance measure, the derived robust kernel is shown to be insensitive to the influence of outliers and robust to intra-class deformations. in the experiments, we justify our robust kernel with different kernel machines with applications to handwritten digit recognition and data visualization on the usps database.
joint power allocation based on link reliability for mimo systems assisted by relay. a new optimization criterion is proposed to minimize error probability for the proposed joint optimal power allocation (pa) of the mimo systems enhanced by relay in this paper. it is proved that the cost function obtained is only convex with respect to (w.r.t.) the power parameters of the source or those of the relay separately, but not convex w.r.t. the whole parameters. in order to use convex optimization methods with high efficiency to solve this complicated problem, a tight upper bound of the sum mse (mean squared error) is derived, and employed to modify the cost function in order to obtain a convex problem. it is verified through simulation results that the proposed pa scheme outperforms the existing one.
tampering identification using empirical frequency response. with the widespread popularity of digital images and the presence of easy-to-use image editing software, content integrity can no longer be taken for granted, and there is a strong need for techniques that not only detect the presence of tampering but also identify its type. this paper focusses on tampering-type identification and introduces a new approach based on the empirical frequency response (efr) to address this problem. we show that several types of tampering operations, both linear shift invariant (lsi) and non-lsi, can be characterized consistently and distinctly by their efrs. we then extend the approach to estimate the efr for scenarios where only the final image is available. theoretical reasoning supported by experimental results verify the effectiveness of this method for identifying the type of a tampering operation.
low-power application-specific processor for fft computations. in this paper, we describe a processor architecture tailored for radix-4 and mixed-radix fft algorithms, which have lower arithmetic complexity than radix-2 algorithms. the processor is based on transport triggered architecture and several optimizations have been used to improve the energy-efficiency. the processor has been synthesized on a 130nm standard cell technology and analysis show that a programmable solution can possess energy-efficiency comparable to a fixed-function asic.
a factor automaton approach for the forced alignment of long speech recordings. this paper addresses the problem of aligning long speech recordings to their transcripts. previous work has focused on using highly tuned language models trained on the transcripts to reduce the search space. in this paper we propose the use of a factor automaton, a well known method to represent all substrings from a string. this automaton encodes a highly constrained language model trained on the transcripts. we show competitive results with n-gram models in several testing scenarios. preliminary experiments show perfect alignments at a reduced computational load and with a smaller memory footprint when compared to n-gram models.
dominant speech enhancement based on snr-adaptive soft mask filtering. in this paper, we present a snr-adaptive soft mask filter for multi-channel noisy speech enhancement. incorporating frame-by-frame spectral magnitude ratios into the time-frequency(t-f) mask filter framework, the adaptive filter can be designed robust to changing environments. experimental results show that the proposed adaptive mask filter can effectively suppress non-stationary noise components even in a closely-spaced microphone pair. moreover, the soft mask compressed with sigmoidal nonlinearity can reduce musical noises so that improved pesq values are obtained.
distributed signal subspace projection algorithms with maximum convergence rate for sensor networks with topological constraints. the observations gathered by the individual nodes of a sensor network may be unreliable due to malfunctioning, observation noise or low battery level. global reliability is typically recovered by collecting all the measurements in a fusion center which takes proper decisions. however, centralized networks are more vulnerable and prone to congestion around the sink nodes. to relax the congestion problem, decrease the network vulnerability and improve the network efficiency, it is appropriate to bring the decisions at the lowest possible level. in this paper, we propose a distributed algorithm allowing each node to improve the reliability of its own reading thanks to the interaction with the other nodes, assuming that the field monitored by the network is a smooth function. in mathematical terms, this only requires that the useful field belongs to a subspace of dimension smaller than the number of nodes. although fully decentralized, the proposed algorithm is globally optimal, in the sense that it performs the projection of the overall set of observations onto the signal subspace through an iterative decentralized algorithms, that requires minimum convergence time, for any given node coverage.
reduced-rank multiuser relaying (rr-mur) scheme for uplink cdma networks. cooperative relaying has been studied extensively in the literature to exploit spatial diversity gains by having each source transmit its messages through multiple independently fading relay paths. in multiuser systems where multiple sources may access the same set of relays simultaneously, cdma spreading techniques along with multiuser detection schemes have been proposed in the literature to eliminate multiple access interference (mai). in order for each relay to forward messages from all sources, a tremendous increase in dimensions (or spreading gain) is used to accommodate the relay transmissions. to reduce the required bandwidth or dimensions, we propose a reduced-rank multiuser relaying (rr-mur) scheme where the data received from multiple users are first compressed into lower dimensions before being retransmitted. more specifically, linear compression precoders at the relays and decoder at the destination are found by imposing a recursive joint optimization procedure with the objective of minimizing the mean square error (mmse) of the estimate at the destination. we show through numerical simulations that the rr-mur scheme outperforms the often adopted q-selection scheme in terms of increased spectral efficiency.
broadband ml estimation under model order uncertainty. the number of signals plays a crucial role in array processing. the performance of most direction finding algorithms relies strongly on a correctly specified number of signals. when this information is not available, conventional approaches apply information theoretic criteria or multiple hypothesis tests to simultaneously estimate model order and parameter. these methods are usually computationally intensive, since ml estimates are required for a hierarchy of nested models. in the previous work [1], we proposed a computationally efficient solution to avoid this full search procedure and demonstrated its feasibility by extensive simulations. here we extend [1] to broadband data, and address issues unique to the broadband case. our max-search approach computes ml estimates only for the maximally hypothesized number of signals, and selects relevant components through hypothesis testing. another novelty of this work is the reduction of indistinguishable components caused by overparameterization. our approach is based on the rank of the estimated steering matrix. numerical experiments show that despite an unknown number of signals, the proposed method achieves comparable estimation and detection accuracy as standard methods, but at much lower computational expense.
decentralized variational filtering for simultaneous sensor localization and target tracking in binary sensor networks. resource limitations in wireless sensor networks have put stringent constraints on distributed signal processing. in this paper, we propose a cluster-based decentralized variational filtering algorithm with minimum resource allocation for simultaneous sensor localization and target tracking. at each sampling instant, only one cluster of sensors is activated according to the prediction of the target state. slave sensors employ a binary proximity observation model to reduce energy consumption and minimize communication cost. based on the binary measurements between sensors and the target, activated sensors and target location estimates are interdependently improved. by adopting the variational method, the inter-cluster information exchange is reduced to one single gaussian statistic, further minimizing resource consumption in the network. since the measurement incorporation and the approximation of the filtering distribution are jointly performed by variational calculus, an effective and lossless compression is achieved compared to the classical particle filtering. effectiveness of the proposed approach is evaluated in terms of tracking accuracy and localization precision.
compressive spectral estimation for nonstationary random processes. we propose a “compressive” estimator of the wigner-ville spectrum (wvs) for time-frequency sparse, underspread, nonstationary random processes. a novel wvs estimator involving the signal's gabor coefficients on an undersampled time-frequency grid is combined with a compressed sensing transformation in order to reduce the number of measurements required. the performance of the compressive wvs estimator is analyzed via a bound on the mean square error and through simulations. we also propose an efficient implementation using a special construction of the measurement matrix.
efficient combination of likelihood recycling and batch calculation based on conditional fast processing and acoustic back-off. this paper proposes an efficient combination of state likelihood recycling and batch state likelihood calculation for accelerating acoustic likelihood calculation in an hmm-based speech recognizer. recycling and batch calculation are each based on different technical approaches, i.e. the former is a purely algorithmic technique while the latter fully exploits pc architecture, and their good acceleration performances are reported in the literatures, respectively. to accelerate the recognition process further by combining them efficiently, we introduce conditional fast processing and acoustic back-off strategies. our combination algorithm employs the conditional fast processing strategy that is conditioned by two criteria. the first potential activity criterion is used to control not only the recycling of state likelihoods at the current frame but also the precalculation of state likelihoods for several succeeding frames. the second reliability criterion and acoustic back-off are used to control the choice of recycled or batch calculated state likelihoods when they are contradictory in the combination and to prevent word accuracies from degrading. large vocabulary spontaneous speech recognition experiments using four pcs with different specifications showed that, despite the pc specification dependence, the combined acceleration technique further reduced the total recognition time on all of the pcs.
cmos compressed imaging by random convolution. we present a cmos imager with built-in capability to perform compressed sensing coding by random convolution. it is achieved by a shift register set in a pseudo-random configuration. it acts as a convolutive filter on the imager focal plane, the current issued from each cmos pixel undergoing a pseudo-random redirection controlled by each component of the filter sequence. a pseudo-random triggering of the adc reading is finally applied to complete the acquisition model. the feasibility of the imager and its robustness under noise and non-linearities have been confirmed by computer simulations, as well as the reconstruction tools supporting the compressed sensing theory.
a new look into the issue of the cramér-rao bound for delay estimation of digitally modulated signals. the cramér-rao bound (crb) with its modifications is a well known lower bound on the variance of any unbiased estimator of an unknown parameter. focusing on the estimation of the time-delay experienced by a digitally modulated signal, we first introduce an approximation of the modified cramér-rao bound (mcrb) as a function of the spectral properties of the modulated signal, irrespective of the specific format of the digital modulation. in particular, we show the equivalence of the mcrb for single carrier modulation and for multicarrier modulation. as a corollary, it is shown that multicarrier signals with uneven power distribution and/or non-contiguous frequency allocation bear a smaller mcrb than equally spaced and powered modulations.
reducing f0 frame error of f0 tracking algorithms under noisy conditions with an unvoiced/voiced classification frontend. in this paper, we propose an f0 frame error (ffe) metric which combines gross pitch error (gpe) and voicing decision error (vde) to objectively evaluate the performance of fundamental frequency (f0) tracking methods. a gpe-vde curve is then developed to show the trade-off between gpe and vde. in addition, we introduce a model-based unvoiced/voiced (u/v) classification frontend which can be used by any f0 tracking algorithm. in the u/v classification, we train speaker independent u/v models, and then adapt them to speaker dependent models in an unsupervised fashion. the u/v classification result is taken as a mask for f0 tracking. experiments using the keele corpus with additive noise show that our statistically-based u/v classifier can reduce vde and ffe for the pitch tracker tempo [1] in both white and babble noise conditions, and that minimizing ffe instead of vde results in a reduction in error rates for a number of f0 tracking algorithms, especially in babble noise.
fusion of fmri, smri, and eeg data using canonical correlation analysis. typically data acquired through imaging techniques such as functional magnetic resonance imaging (fmri), structural mri (smri), and electroencephalography (eeg) are analyzed separately. each modality records brain structure and function at different scales, and fusing information from such complementary modalities promises to provide additional insight into connectivity across brain networks and changes due to disease. recently, a number of methods have been proposed for data integration and fusion of two brain imaging modalities. we propose a new data fusion scheme based on canonical correlation analysis that enables the detection of associations across multiple modalities. our multimodal canonical correlation analysis (mcca) scheme works at the feature level using multi-set cca to determine inter-subject covariations across modalities. we apply mcca to fmri, smri, and eeg data collected from patients diagnosed with schizophrenia and healthy controls. through data collected from an auditory oddball task, we show that the fusion of multiple modalities detects more specific associations as compared to fusion of two modalities.
interval consensus: from quantized gossip to voting. we design distributed and quantized average consensus algorithms on arbitrary connected networks. by construction, quantized algorithms cannot produce a real, analog average. instead, our algorithm reaches consensus on the quantized interval that contains the average. we prove that this consensus in reached in finite time almost surely. as a byproduct of this convergence result, we show that the majority voting problem is solvable with only 2 bits of memory per agent.
a simple, efficient and near optimal algorithm for compressed sensing. when sampling signals below the nyquist rate, efficient and accurate reconstruction is nevertheless possible, whenever the sampling system is well behaved and the signal is well approximated by a sparse vector. this statement has been formalised in the recently developed theory of compressed sensing, which developed conditions on the sampling system and proved the performance of several efficient algorithms for signal reconstruction under these conditions. in this paper, we prove that a very simple and efficient algorithm, known as iterative hard thresholding, has near optimal performance guarantees rivalling those derived for other state of the art approaches.
sub-band stap for stretch processed systems. this paper considers adaptive jammer rejection algorithms applicable to wideband stretch-processed systems. specifically, sub-banding algorithms in which the received data is first pre-processed into narrow frequency bins are studied. in conventional sub-band stap, the received signal is divided into narrowband frequency bins, following which the interference is adaptively rejected in each bin. this usually requires that a different weight vector be computed for each sub-band. alternatively, the received data can be pre-processed so that every sub-band is shifted to a common central sub-band. this enables computation of a single weight vector that can be applied to all of the shifted sub-bands. simulation results are presented to assess the behavior and performance of these algorithms.
classification between normal and abnormal respiratory sounds based on maximum likelihood approach. in this paper, we have proposed a novel classification procedure for distinguishing between normal respiratory and abnormal respiratory sounds based on a maximum likelihood approach using hidden markov models. we have assumed that each inspiratory/expiratory period consists of a time sequence of characteristic acoustic segments. the classification procedure detects the segment sequence with the highest likelihood and yields the classification result. we have proposed two elaborate acoustic modeling methods: one method is individual modeling for adventitious sound periods and for breath sound periods for the detection of abnormal respiratory sounds, and the other is a microphone-dependent modeling method for the detection of normal respiratory sounds. classification experiments conducted using the former method revealed that this method demonstrated an increase of 19.1% in its recall rate of abnormal respiratory sounds as compared with the recall rate of a baseline method. it has also been revealed that the latter modeling method demonstrates an increase in its recall rate for the detection of not only normal respiratory sounds but also for abnormal respiratory sounds. these experimental results have confirmed the validity of our proposed classification procedure.
on radius control of tree-pruned sphere decoding. in this paper, we propose a novel radius control strategy for sphere decoding referred to as inter search radius control that provides further improvement of the computational complexity with minimal extra cost and negligible performance penalty. the proposed method focuses on the sphere radius control strategy when a candidate lattice point is found. for this purpose, the dynamic radius update strategy as well as the lattice independent radius selection scheme are jointly exploited. from simulations in multiple-input and multiple-output (mimo) channels, it is shown that the proposed method provides a substantial improvement in complexity with near-ml performance.
sparse approximation with an orthogonal complementary matching pursuit algorithm. this paper presents the orthogonal extension of the recently introduced complementary matching pursuit (cmp) algorithm for sparse approximation [1]. the cmp algorithm is analogous to the matching pursuit (mp) but done in the row-space of the dictionary matrix. it suffers from a similar sub-optimality as the mp. the orthogonal complementary matching pursuit algorithm (ocmp) presented here tries to remove this sub-optimality by updating the coefficients of all selected atoms at each iteration. its development from the cmp follows the same procedure as of the orthogonal matching pursuit (omp). in contrast with omp, the residual errors resulting from the ocmp may not be orthogonal to all the atoms selected up to the respective iteration. though the residual energy may increase over the omp during the first iterations, it is shown that, compared with omp, the convergence speed is increased in the subsequent iterations and the sparsity of the solution vector is improved.
postural time-series analysis using empirical mode decomposition and second-order difference plots. this paper presents a new method for analysis of center of pressure (cop) signals using empirical mode decomposition (emd). the emd decomposes a cop signal into a finite set of band-limited signals termed as intrinsic mode functions (imfs). thereafter, a signal processing technique used in continuous chaotic modeling is used to investigate the difference between experimental conditions on the summed imfs. this method is used to detect the degree of variability from a second-order difference plot, which is quantified using a central tendency measure (ctm). seventeen subjects were tested under eyes open (eo) and eyes closed (ec) conditions, with different vibration frequencies applied for the ec condition in order to provide additional sensory perturbation. this study has demonstrated an effective way to differentiate vibration frequencies by combining emd and second-order difference (sod) plots.