waashk/dblp huggingface dataset:
fixed sinr solutions for the mimo wiretap channel. this paper studies the use of artificial interference in reducing the likelihood that a message transmitted between two multi-antenna nodes is intercepted by an undetected eavesdropper. unlike previous work that assumes some prior knowledge of the eavesdropper's channel and focuses on the information theoretic concept of secrecy capacity, we also consider the case where no information regarding the eavesdropper is present, and we use the relative signal-to-interference-plus-noise-ratio (sinr) of a single transmitted data stream as our performance metric. a portion of the transmit power is used to broadcast the information signal with just enough power to guarantee a certain sinr at the desired receiver, and the remainder of the power is used to broadcast artificial noise in order to mask the desired signal from a potential eavesdropper. the interference is designed to be orthogonal to the information signal when it reaches the desired receiver, and we study the resulting relative sinr of the desired receiver and the eavesdropper assuming both employ optimal beamformers.
optimal geometry configuration of bistatic forward-looking sar. with appropriate geometry configurations, bistatic synthetic aperture radar (sar) can break through the limitations of monostatic sar on forward-looking imaging. with such a capability, bistatic forward-looking sar (bfsar) has extensive potential applications. in this paper, based on the resolution calculation using gradient theory, we give a general rule to determine the optimal geometry configuration of different modes of bfsar. the results can be used to design bfsar flight campaign and measure the performance of a specific bfsar system.
tracking forecast memories in stochastic decoders. this paper proposes tracking forecast memories (tfms) as a novel method for implementing re-randomization and decorrelation of stochastic bit streams in stochastic channel decoders. we show that tfms are able to achieve decoding performance similar to that of the previous methods in the literature (i.e., edge memories or ems), but they exhibit much lower hardware complexity. tfms significantly reduce the area requirements of asic implementations of stochastic decoders.
estimating correspondence between multiple cameras using joint invariants. the joint invariants of the projective group psl(3,ℝ) on ℝℙ2, the five-point volume cross-ratios, are studied to address the problem of correspondence in a camera network. the distribution of cross-ratios over the unit square as well as in a small local-neighbourhood of a reference point are found to have a heavy tail. no cross ratio value is unique but the collection of five point cross ratios generated by taking all possible combination of five points completely prescribes the curve. sections of the signature submanifold that admit large enough variation of cross ratios are found to be sufficient in providing correspondence across wide perspectives. such invariant signatures may be collected independently at cameras with different viewpoints and shared, thereby achieving the registration of objects in the image. experimental results with license plate database are provided.
exploring the automatic mispronunciation detection of confusable phones for mandarin. mispronunciation detection is one of the vital tasks of the call (computer assisted language learning) systems. many methods have been introduced to accomplish this task. however, few of them have addressed the detection task on confusable phones. in this paper, phone-level classifiers are utilized to improve the detection performance on the confusable phones. features of the classifiers are posterior probability vectors calculated from their corresponding acoustic models. moreover, confusion matrix is also extracted and incorporated to calculate derivatives of the posterior probability vectors. experiments on our mandarin database validate the effectiveness of our proposed method, compared with the commonly used posterior probability and phone dependent thresholds methods.
joint optimization of the redundancy of multiple-description coders for multicast. we consider the optimization of multicast over packet-switched communication networks with a non-zero packet-loss probability. for the system setup consisting of a number of multiple-description coders, we jointly optimize these coders. we propose an analytic solution, asymptotically optimal in the number of multiple-description coders. the analytic solution allows for fast system adaptation to changing network conditions. a locally optimal optimization algorithm that is useful when the number of multicast groups is small is derived. simulations show that the utilization of the analytic solution incurs a low overhead on the performance when compared to the locally optimal solution, even for a small number of multiple-description coders.
gridnews: a distributed automatic greek broadcast transcription system. in this paper, a distributed system storing and retrieving broadcast news data recorded from the greek television is presented. these multimodal data are processed in a grid computational environment interconnecting distributed data storage and processing subsystems. the innovative element of this system is the implementation of the signal processing algorithms in this grid environment, offering additional flexibility and computational power. among the developed signal processing modules are: the segmentor, cutting up the original videos into shorter ones, the classifier, recognizing whether these short videos contain speech or not, the greek large-vocabulary speech recognizer, transcribing speech into written text, and finally the text search engine and the video retriever. all the processed data are stored and retrieved in geographically distributed storage elements. a user-friendly, web-based interface is developed, facilitating the transparent import and storage of new multimodal data, their off-line processing and finally, their search and retrieval.
combining frontend-based memory with mfcc features for bandwidth extension of narrowband speech. in this paper, we continue our previous work on improving bandwidth extension (bwe) of narrowband speech. we have shown that including memory into the parametrization frontend (through delta features) results in higher highband certainty irrespective of feature type, with mfccs exhibiting higher correlation, in general, between both bands, reaching twice that using lsfs. by incorporating memory into the frontend of a conventional lp-based bwe system, we were able to translate the higher correlation due to memory into bwe performance improvement. using high-resolution inverse dct, we also achieved high quality speech reconstruction from mfccs, thus enabling mfcc-based bwe with improved performance compared to conventional static lp-based bwe. we continue this work by incorporating the superior correlation properties of frontend memory into our mfcc-based bwe system. log-spectral distortion as well as the more perceptually-correlated itakura-based measures show that incorporating memory into our mfcc-based bwe system results in bwe performance superior to that of our dynamic lp-based bwe system.
semi-tied covariance matrices for acoustic models based on random forests of phonetic decision trees. in this paper, we investigate combining semi-tied covariance matrices and random forests (rfs) based phonetic decision trees (pdts) for acoustic modeling in conversational speech recognition. we first use the rf method to train multiple pdts for each phone state unit, and generate multiple sets of acoustic models accordingly. we then apply semi-tied covariance matrices to each set of acoustic models to improve their fit to data. in decoding search we combine the likelihood scores from the multiple acoustic models for each speech frame. the viability of semi-tied covariance matrices with different tying classes are studied from their effects on the diversity of rf-based acoustic models as well as on the word accuracy of our task of telehealth automatic captioning. experimental results indicate that semi-tied covariance matrices help enhance the diversity of the rfs-pdts based acoustic models as well as increase word accuracy.
multi-view tracking of articulated human motion in silhouette and pose manifolds. this paper presents a multi-view articulated human motion tracking framework using particle filter with manifold learning through gaussian process latent variable model. the dimensionality of the input image observation and joint angles are reduced using gaussian process models to improve the tracking efficiency. the forward and backward mappings between the two low dimensional spaces are then obtained using relevance vector machine and batesian mixture of experts (bme). improved sampling schemes and auto-initialization are obtained using bme.without using a 3d body model, effective likelihood evaluation is obtained through rvm using images from multiple views. tracking results obtained using real videos with complex dance movement show the efficacy of the proposed approach.
an approximate l0 norm minimization algorithm for compressed sensing. ℓ0 norm based signal recovery is attractive in compressed sensing as it can facilitate exact recovery of sparse signal with very high probability. unfortunately, direct ℓ0 norm minimization problem is np-hard. this paper describes an approximate ℓ0 norm algorithm for sparse representation which preserves most of the advantages of ℓ<sup>0 norm. the algorithm shows attractive convergence properties, and provides remarkable performance improvement in noisy environment compared to other popular algorithms. the sparse representation algorithm presented is capable of very fast signal recovery, thereby reducing retrieval latency when handling
adaptive reconstruction method of missing textures based on kernel canonical correlation analysis. this paper presents an adaptive reconstruction method of missing textures based on kernel canonical correlation analysis (cca). the proposed method calculates the correlation between two areas, which respectively correspond to a missing area and its neighbor area, from known parts within the target image and realizes the estimation of the missing textures. in order to obtain this correlation, the kernel cca is applied to each set containing the same kind of textures, and the optimal result is selected for the target missing area. specifically, a new approach monitoring errors caused in the above estimation process enables the selection of the optimal result. this approach provides a solution to the problem in traditional methods of not being able to perform adaptive reconstruction of the target textures due to the missing intensities. experimental results show subjective and quantitative improvement of the proposed reconstruction technique over previously reported reconstruction techniques.
phoneme recognition using spectral envelope and modulation frequency features. we present a new feature extraction technique for phoneme recognition that uses short-term spectral envelope and modulation frequency features. these features are derived from sub-band temporal envelopes of speech estimated using frequency domain linear prediction (fdlp). while spectral envelope features are obtained by the short-term integration of the sub-band envelopes, the modulation frequency components are derived from the long-term evolution of the sub-band envelopes. these features are combined at the phoneme posterior level and used as features for a hybrid hmm-ann phoneme recognizer. for the phoneme recognition task on the timit database, the proposed features show an improvement of 4.7% over the other feature extraction techniques.
a complex cross-spectral distribution model using normal variance mean mixtures. we propose a model for the density of cross-spectral coefficients using normal variance mean mixtures. we show that this model density generalizes the corresponding marginal density of the complex wishart distribution for the cross-spectral density. the maximum likelihood estimate of parameters in the distribution is derived, and examples are given from alpha brain wave sources in separated eeg data.
an algebraic polyphase approach to wireless network coding. network coding has been shown to improve throughput, minimize delay and economize the energy requirements in wireless networks. this paper presents an algebraic polyphase approach to the wireless linear network coding problem. by modeling wireless nodes as consisting of linear periodic time varying filters, the model incorporates realistic constraints including omni directionality of transmissions, half-duplex operation and interference effects. a rank criterion is introduced, which together with the transmission constraints, constitutes the necessary and sufficient conditions for the existence of a wireless network code.
multi-layer ratio semi-definite classifiers. we develop a novel extension to the ratio semi-definite classifier, a discriminative model formulated as a ratio of semi-definite polynomials. by adding a hidden layer to the model, we can efficiently train the model, while achieving higher accuracy than the original version. results on artificial 2-d data as well as two separate phone classification corpora show that our multi-layer model still avoids the overconfidence bias found in models based on ratios of exponentials, while remaining competitive with state-of-the-art techniques such as multi-layer perceptrons.
game theory for precoding in a multi-user system: bargaining for overall benefits. a precoding strategy for multi-user spectrum sharing over an interference channel is proposed and analyzed from a game-theoretic perspective. the proposed strategy is based on finding the nash bargaining solution for precoding matrices in a cooperative scenario over frequency selective channels under a spectrum mask constraint. an in-time update of the precoding matrices is enabled by using time slots to guarantee the effectiveness of the bargaining solution when the number of users varies. a dual decomposition approach is exploited to construct a distributed structure for solving the bargaining problem. the proposed distributed algorithm realizes the physical process of bargaining, which is not present in the nash bargaining theory.
a new perceptual quality metric for compressed video. this paper presents a new video quality metric for automatically estimating the perceptual quality of compressed video sequences. distortion measures such as the mean squared error (mse) and the peak signal to noise ratio (psnr) have been found to poorly correlate with visual quality at lower bit-rates. the proposed quality metric (mosp) predicts perceptual quality of compressed video using sequence characteristics and the mean squared error (mse) between the original and compressed video sequences. the metric has been tested on various video sequences compressed using the h.264 video compression standard at different bit-rates. results show that the proposed metric has better correlation with subjective quality compared to popular metrics such as psnr, ssim and psnrplus. the new metric is simple to compute and hence suitable for incorporation into real-time applications such as the standard video compression codecs inorder to improve the visual quality of compressed video sequences.
on the role of localization cues in binaural segregation of reverberant speech. approaches to binaural and stereo speech segregation have often assumed that localization information can be used as a primary cue to achieve segregation of a target signal. results produced by these systems degrade significantly in the presence of room reverberation. in this work, we present an alternative framework to achieve localization of groups of time-frequency units. we show that grouping across time and frequency allows the use of localization as an important cue for sequential grouping of time-frequency objects. we analyze the level of time-frequency grouping needed to achieve accurate object localization and show preliminary binaural segregation results using the proposed framework. results indicate that both localization and segregation performance can be improved by grouping across time and frequency.
memory characterization to analyze and predict multimedia performance and power in embedded systems. main memory performance and power are becoming increasingly critical for embedded and general purpose computing systems. that is the primary motivation for us to focus on analyzing and modeling memory utilization for multimedia applications in embedded systems like cell-phones and pdas with limited hardware resources and battery supply. in addition, the paper presents scenarios of utilizing the memory characteristic model to predict multimedia application performance, optimize embedded software, and adapt hardware resources to save system power while guaranteeing adequate performance. we present a simple, but effective indicator to identify the application's dynamic memory and computational composition. using this, we can easily and accurately forecast application performance and help determine optimal resources needed by the multimedia applications.
motion-compensated techniques for enhancement of low-quality compressed videos. algorithms for enhancement of low quality compressed videos are described and evaluated in this paper. cascaded and combined spatio-temporal filters using hierarchical motion estimation and occlusion area detection are investigated for removal of severe coding artifacts and temporal flickering. both objective and subjective evaluations prove that temporal filtering can significantly improve the video quality of low bit rate video sequences. the proposed methods could therefore be a differentiating feature for future iptv design.
speech enhancement in car noise envoronment based on an analysis-synthesis approach using harmonic noise model. this paper presents a speech enhancement method based on an analysis-synthesis framework using harmonic noise model (hnm) in car noise environment. the major advantages of this method are effective suppression of car noise even in very low signal-to-noise ratio environments and mitigation of “musical tones” which are generally introduced by conventional methods. in this paper, we devise a complete analysis-synthesis based speech enhancement system, and give details in hnm modeling, parameter estimation, and car noise adaptation. subjective evaluation results show that the proposed method exhibits better noise suppression ability over conventional approaches without obvious degradation of speech quality.
artifact removal in eeg using morphological component analysis. to reduce the effects of artifacts in electroencephalography (eeg), we propose the use of morphological component analysis (mca). taking advantage of the sparse representation of data in overcomplete dictionaries, mca decomposes eeg signals into parts that have different morphological characteristics. for denoising purpose, the parts related to artifacts are removed. an overcomplete dictionary is constructed using the discrete cosine transform, daubechies wavelet basis, and dirac basis. movement-related potentials (mrp) and eeg signals contaminated by spikes, eye-blinks, and muscle artifacts caused by eye-brow raising are used to evaluate the performance of the method. the results demonstrate that mca can be used to decompose the single-channel eeg signals into artifacts and mrp components. the correlation coefficient between the denoised mrp and the original mrp using mca is significantly higher than that obtained using stationary wavelet transform.
a new optimization algorithm for network component analysis based on convex programming. network component analysis (nca) has been established as a promising tool for reconstructing gene regulatory networks from microarray data. nca is a method that can resolve the problem of blind source separation when the mixing matrix instead has a known sparse structure despite the correlation among the source signals. the original nca algorithm relies on alternating least squares (als) and suffers from local convergence as well as slow convergence. in this paper, we develop new and more robust nca algorithms by incorporating additional signal constraints. in particular, we introduce the biologically sound constraints that all nonzero entries in the connectivity network are positive. our new approach formulates a convex optimization problem which can be solved efficiently and effectively by fast convex programming algorithms. we verify the effectiveness and robustness of our new approach using simulations and gene regulatory network reconstruction from experimental yeast cell cycle microarray data.
energy-efficient transmission of h.264 scalable video over ieee 802.11e. achieving low energy consumption is one of the main challenges for wireless video transmission on battery-limited devices. moreover, the bandwidth is scarce and must be shared efficiently among users. the focus in this paper is on the timely delivery of multiple delay-sensitive video flows over a distributed access wireless lan with minimal energy cost. this is done taking into consideration the enhanced distributed channel access (edca) mode and the scalable video codec (svc). in this context, a method is presented for energy-efficient resource allocation across the physical layer and medium access layer, by properly leveraging transmission modes and the available prioritization mechanisms. global energy savings around 60% are achieved with respect to state-of-the-art edca under a wide range of network loads.
pulse coupled oscillators' primitive for low complexity scheduling. pulse coupled oscillators (pcos) are pulsing devices that pulse individually in a periodic manner but alter their pulsing patterns in response to the pulsing of other nodes. a network of pcos can produce a number of different dynamics from their pulsing activities, among which the synchrony of pulsing is perhaps the most well known. in this paper, we study the primitive that falls into the class of “desynchronization”. specifically, we propose a simple pulse-coupling mechanism that allows each node in the network to converge to a desynchronized state where the nodes will pulse periodically with a constant spacing among each others firing times. we discuss the convergence of the pco mechanism and propose to apply this primitive to resolve contention in the reservation phase of a reservation-based mac protocol.
phmm based asynchronous acoustic model for chinese large vocabulary continuous speech recognition. in this paper, we presented an asynchronous multiple stream based chinese tonal acoustic modeling framework. in this framework, toneless phonetic units and tones are modeled separately with different acoustic features. during the training and decoding process, a set of models are coupled together with a product hidden markov models (phmm) to represent whole tonal phonetic units. through this, a compound context dependent tonal model can be generated from a few simple models. experiments show that such model scheme generates more compact and accurate model presentation and brings improvement on the performance for large vocabulary speech recognition tasks.
dct based multiple hashing technique for robust audio fingerprinting. audio fingerprinting techniques should successfully perform content-based audio identification even when the audio files are slightly or seriously distorted. in this paper, we present a novel audio fingerprinting technique based on combining fingerprint matching results for multiple hash tables in order to improve the robustness of hashing. multiple hash tables are built based on the discrete cosine transform (dct) which is applied to the time sequence of energies in each sub-band. experimental results show that the recognition errors are significantly reduced compared with philips robust hash (prh) [1] under various distortions.