nils-herrmann/clean_scirepeval huggingface dataset:
Cyclic articulated human motion tracking by sequential ancestral simulation

Accurate tracking of cyclic human motion in video data helps in developing computer-aided applications such as gait analysis, visual surveillance, patient rehabilitation, etc. This paper presents a novel technique for tracking cyclic human motion based on decomposing complex cyclic motion into components and maintaining coupling between components. The decomposition reduces the dimensionality of the problem and enables a graphical modeling of the articulated human body. The coupling between components is modeled by their phase relationship and represented as directed edges in Bayesian networks and undirected edges in Markov random fields. Such coupling is maintained in tracking through ancestral simulation (AS) and Markov potentials in a sequential Monte Carlo tracking framework. We show that the approach handles severe self-occlusion and foreign body occlusion with improved accuracy and efficiency.
Compensated Signature Embedding Based Multimedia Content Authentication System

Digital content authentication and preservation is an extremely challenging task in realizing decentralized digital libraries. The concept of compensated signature embedding is proposed to develop an effective multimedia content authentication system. The proposed system does not require any third party reference or side information. Towards this end, a content-based fragile signature is derived and embedded into the media using a robust watermarking technique. Since the embedding process introduces distortion in the media, it may lead to authentication failure. We propose to adjust the media samples iteratively or using a closed form process to compensate for the embedding distortion. Using an example image authentication system, we show that the proposed scheme is highly effective in detecting even minor modifications to the media.
Data-hiding in audio using frequency-selective phase alteration

A novel perception-based data hiding technique for digital audio is proposed. It exploits the lower sensitivity of the human auditory system (HAS) to phase distortion in audio compared with magnitude distortion. Audio is decomposed into subband signals, some of which are selected for embedding data with a controlled alteration of phase using suitable allpass digital filters. The proposed scheme is robust to standard data manipulations yielding less than 2% error probability against compression, re-sampling, re-quantization, random chopping and noise addition. The proposed method is also robust to desynchronization attacks.
Regularized Estimation of Retinal Vascular Oxygen Tension From Phosphorescence Images

The level of retinal oxygenation is potentially an important cue to the onset or presence of some common retinal diseases. An improved method for assessing oxygen tension in retinal blood vessels from phosphorescence lifetime imaging data is reported in this paper. The optimum estimate for phosphorescence lifetime and oxygen tension is obtained by regularizing the least-squares (LS) method. The estimation method is implemented with an iterative algorithm to minimize a regularized LS cost function. The effectiveness of the proposed method is demonstrated by applying it to simulated data as well as image data acquired from rat retinas. The method is shown to yield estimates that are robust to noise and whose variance is lower than that obtained with the classical LS method.
Multiple description coding over correlated multipath erasure channels

The problem of optimal rate allocation to streams constituting multiple description coding (MDC) has largely been addressed under the assumption that the multiple paths available for transmission are uncorrelated. In this paper, we model the transmission paths as correlated erasure channels and then examine the problem of allocating a given coding rate to the multiple descriptions in order to minimize the average distortion of the reconstructed message. We also investigate the relationship between the optimal average distortion and correlation of channels and prove that the bound on the average distortion will decrease as the correlation increases. Furthermore, we derive a closed-form solution for the contour which determines the region where multiple description coding (MDC) or single description coding (SDC) yields the minimal bound on the average distortion. Relying on this contour, we present a heuristic of the optimal rate allocation problem to determine the number of descriptions and relative rates.
Efficient sampling rate alteration using recursive (IIR) digital filters

A general scheme for changing sampling rates is developed, and a method of designing recursive (IIR) filters for use in this general scheme is presented. If phase response is ignored, then this scheme provides a considerable savings in computations over nonrecursive (FIR) schemes. The use of recursive filters with approximately linear phase also led to a performance better than that using nonrecursive filters. The quantization effects in the new scheme are minimal.
Performance evaluation of classifier techniques to discriminate odors with an E-Nose

The foremost objective of this research is to discriminate odors with an E-Nose using various classifier technique based on statistical machine learning theory. The goal of learning is prediction. The learning problem comprises of the purpose that is used to map between the input and the output in a predictive manner, such that the output can be predicted from future input using the function that is learnt. The TGS series gas sensors (TGS 2602, TGS2620, TGS2201A and TGS2201B) are employed which are stimuli to several indoor air contaminants. With the help of the classifier techniques we will differentiate the gases and other organic compounds. As to this four techniques have been used as a classifier in this context such as PCA, LDA, SVM and NBC. The learning techniques are categorized into supervised and non-supervised. The comparisons among various techniques have been drawn to differentiate the techniques on the basis of classification accuracy.
HDTV subband/DCT coding: analysis of system complexity

A hybrid algorithm has been demonstrated to be an efficient way to encode high-definition television sequences, where each field of the sequence is decomposed into four subbands, with discrete cosine transform applied to the low-frequency subband. The system implementation complexity of such an algorithm and the implementation of low-order filters that perform the subband analysis and synthesis are analyzed. The authors also examine some issues in the connection of the codec to the asynchronous time multiplexing layer for transmission on the CCITT-defined H4 channel. It is shown that a low-cost implementation of the subband/DCT codec for the transmission of distribution quality compressed digital HDTV is feasible at a rate of 135 Mb/s.<<ETX>>
Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS) for Non-Smooth Data Field

Compressive Sensing (CS) has been applied successfully in a wide variety of applications in recent years, including photography, holography, optical system research, facial recognition, and Medical Resonance Imaging (MRI). In wireless sensor networks (WSNs), significant research work has been pursued to investigate the use of CS to reduce the amount of data communicated, particularly in data aggregation applications and thereby improving energy efficiency. However, most of the previous work in WSN has used CS under the assumption that data field is smooth with negligible white Gaussian noise. In these schemes signal sparsity is estimated globally based on the entire data field, which is then used to determine the CS parameters. In more realistic scenarios, where data field may have regional fluctuations or it is piecewise smooth, existing CS based data aggregation schemes yield poor compression efficiency. In order to take full advantage of CS in WSNs, we propose an adaptive aggregation scheme referred to as Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS). The proposed schemes dynamically determines sparsity values based on signal variations in local regions. We prove that A-HDACS enables more sensor nodes to employ CS compared to the schemes that do not adapt to the changing field. Also, the simulation results demonstrate improvement in energy efficiency and accuracy in signal recovery.
The Effect of Fading Correlation on Average Source MMSE Distortion

This paper considers the end-to-end mean-square distortion in reconstructing a memoryless proper-complex Gaussian source transmitted over parallel block-fading Rayleigh AWGN channels. We characterize the distortion exponent of several source and channel coding strategies; that is, we characterize how fast the distortion decays to zero as the SNR increases. Unlike previous works, we consider networks with different received SNR's and with correlated fading. We generalize the definition of distortion exponent to SNR-asymmetric channels. We show that fading correlation degrades the achievable mean-square distortion but does not affect the distortion exponent. The performance degradation is measured in terms of power-offset; that is, the power increment needed to achieve the same performance as the uncorrelated case. We show that the power-offset is proportional to the determinant of the fading correlation matrix. Our proposed methodology allows us to study any number of parallel channels and we are no longer restricted to two channels (as was commonly done in the previous literature). Finally, we show that determining the distortion exponent of Multiple Description Coding (MDC) schemes in high SNR reduces to solving a linear programming problem.
Underground imaging using RF tomography: The effect of lateral waves

Radio Frequency (RF) tomography is a novel strategy for underground imaging over relatively wide and deep surveillance areas. RF tomography requires a set of low-cost transmitters and receivers deployed randomly on the surface of the ground, or slightly buried. Using the principles of inverse scattering, it is possible to image below ground high contrast dielectric or conducting anomalies, thus revealing buried objects and hidden targets. In RF Tomography, when antennas are buried, or targets are in shallow regions, waves are propagating in proximity of the air-earth interface; therefore, it is necessary to account the Green's function of the half-space and the interference effect of lateral waves. In this paper we introduce an improved inversion scheme that is robust with respect to noise and attenuation due to lossy soils. The effectiveness of this inversion strategy is shown by presenting images reconstructed via synthetic data.
Detection of microcalcifications in mammograms using higher order statistics

A new method for detecting microcalcifications in mammograms is described. In this method, the mammogram image is first processed by a subband decomposition filterbank. The bandpass subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. The detection method utilizes these two parameters. A region with high positive skewness and kurtosis is marked as a region of interest. Simulation results show that this method is successful in detecting regions with microcalcifications.
Power-efficient hierarchical data aggregation using compressive sensing in WSNs

Compressive sensing (CS) is a burgeoning technique being applied to diverse areas including wireless sensor networks (WSNs). In WSNs, it has been applied in the context of data gathering and aggregation, particularly aimed at reducing data transmission cost and improving power efficiency. Existing CS-based data gathering work in WSNs utilize the property that under certain conditions, only O(K log N) CS random measurements can represent a K-sparse signal of length N. In previous work fixed and identical compression thresholds were assumed for the entire network resulting in less efficient solutions. In this paper, we present a novel data aggregation architecture model that integrates a multi-resolution hierarchical structure with CS to further optimize the amount of data transmitted. Our key idea is to set up multiple compression thresholds adaptively based on the cluster sizes at different levels. The advantages of the proposed aggregation model in contrast to other state-of-the-art related work are measured in terms of total amount of data for transmission, data compression ratio and energy consumption. We implement the proposed data aggregation scheme on a SIDnet-SWANS platform, a discrete event simulator commonly used for WSN simulations. Our simulation results demonstrate that the proposed CS-based hierarchical data aggregation model guarantees accurate signal recovery performance; meanwhile, it also obtains substantial energy savings compared to other existing methods.
Antenna Selection and Power Control for Limited Feedback MIMO Systems

While antenna selection has been shown to be effective in improving bit error rate (BER) performance in multiple-input-multiple-output (MIMO) systems, further performance gain can be obtained by transmitting only when the channel gains are sufficiently high and by using power control to compensate for channel variations. Truncated channel inversion is an adaptive power control scheme that compensates for fading above a certain cutoff fade depth: below the cutoff level the transmission is stopped. In this paper, we propose a novel truncated channel inversion power control based antenna selection scheme to improve the BER performance of spatial multiplexing (SM) systems with linear receivers. Power control based optimal and sub-optimal antenna selection criteria are proposed to dynamically select the number of active transmit antennas and the mapping of data substreams to transmit antennas. Simulations show that, in flat Rayleigh fading channels, the proposed power control based antenna selection scheme significantly outperforms the existing schemes in the literature.
Real-time tracking with multiple cues by set theoretic random search

Conventional treatment of visual tracking has been to optimize an objective function in a probabilistic framework. In this formulation, efficient algorithms employing simple prior distributions are usually insufficient to handle clutters (e.g., Kalman filter). On the other hand, distributions that are complex enough to incorporate all a priori knowledge can make the problem computationally intractable (e.g., particle filters (PF)). This paper proposes a new formulation of visual tracking where every piece of information, be it from a priori knowledge or observed data, is represented by a set in the solution space and the intersection of these sets, the feasibility set, represents all acceptable solutions. Based on this formulation, we propose an algorithm whose objective is to find a solution in the feasibility set. We show that this set theoretic tracking algorithm performs effective face tracking and is computationally more efficient than standard PF-based tracking.
Frequency-based local content adaptive filtering algorithm for automated photoreceptor cell density quantification

Photoreceptor cells in the human eye play a vital role in vision. Certain retinal diseases cause the photoreceptor cells to degenerate and may lead to vision loss. Quantification of photoreceptor cell density from adaptive optics (AO) retinal images can provide valuable information and aid in the screening, diagnosis, and follow-up of retinal diseases. In this paper we describe an image model using a windowed two-dimensional (2D) lattice of pulses representing the cells and characterize the frequency content as decaying frequency domain pulses on the reciprocal lattice. Based on this model we propose a novel method for detection of cone photoreceptor cells by analyzing the discrete-space Fourier transform (DSFT) of AO retinal images. This method uses a small-extent block-based 2D discrete Fourier transform (DFT) to determine cell frequency content in order to obtain parameters of an adaptive circularly symmetric band-pass filter that is applied to the image. The filter extracts the underlying cellular structure and removes high-frequency noise as well as very low frequency contamination manifested as slow variations in the image. Subsequent detection yields an automated cell count that compares well with actual and manual counts on test and retinal images and demonstrates the accuracy of the method.
Automated Real-Time Conjunctival Microvasculature Image Stabilization

The bulbar conjunctiva is a thin, vascularized membrane covering the sclera of the eye. Non-invasive imaging techniques have been utilized to assess the conjunctival vasculature as a means of studying microcirculatory hemodynamics. However, eye motion often confounds quantification of these hemodynamic properties. In the current study, we present a novel optical imaging system for automated stabilization of conjunctival microvasculature images by real-time eye motion tracking and realignment of the optical path. The ability of the system to stabilize conjunctival images acquired over time by reducing image displacements and maintaining the imaging area was demonstrated.
A method for detection of retinal layers by optical coherence tomography image segmentation

A novel algorithm is reported for automatically segmenting optical coherence tomography (OCT) images to identify six layers within the retina. The boundary detection technique presented here is based on a 2-D edge detection scheme, in which a filter with wedge-shaped pass band is introduced. The proposed filter enhances edges along the vertical boundaries while suppressing speckle noise. The detected contours are labeled based on a retina model. The layer thickness measurements derived by the algorithm are compared with thickness measurements from manually marked boundaries. The automated thickness measurements derived by the algorithm differ from manual segmentation results by less than 5 microns on average.
Real-time adaptive forward error correction for MPEG-2 video communications over RTP networks

We present an algorithm for real-time adaptive forward error correction (FEC) of MPEG-2 video stream, encapsulated using real-time transport protocol (RTP) and delivered over best-effort networks. Our algorithm provides an efficient method to determine the allocation of redundancy to the MPEG-2 video stream. The redundancy is allocated such that the resulting estimated degradation density function for video (DDF) is uniformly distributed. A weight, which indicates the relative importance of RTP packets, together with the communication channel characteristics and FEC scheme are used to model the density function of the video stream and allow us to determine the allocation of FEC packets. The weight is based on the content of RTP packets in the video stream. Parameters extracted from the RTP header are used to determine the weights, so that the proposed algorithm can be implemented in real-time. In our simulations, we have relied on motion compensation and group of picture (GOP) data to determine the relative weights. Simulation results provided establish the significant improvement in performance based on our proposed approach to adaptive FEC.
Clipping noise suppression in OFDMA using frame based alternating projection

In OFDMA systems, receiver-oriented methods present new challenges in suppressing clipping noise to compensate for power amplifier nonlinearity because not all modulation information about other users is available to any individual user. Recently a scheme based on band limited signal recovery has been proposed to address this problem but its performance in high SNR regime is limited. In this paper, we cast the problem as one of the clipping noise suppression estimation using the framework of frame theory together with alternating projection over convex sets to improve the performance. Extensive simulations show the proposed scheme outperforms existing schemes without increasing complexity, and in particular, system performance in high SNR regime is significantly improved.
Map estimation of oxygen tension in retinal vessels with phosphorescence lifetime imaging

In phosphorescence lifetime imaging methods oxygen tension in retinal vessels has traditionally been indirectly determined from the estimates of intermediate variables whose noise-contaminated linear combinations are observed as phosphorescence intensity images. The classical least squares (LS) and regularized least squares (RLS) methods were used to obtain estimates of the intermediate variables. The estimates of the intermediate variables are then used to compute oxygen tension. The estimates of intermediate variables, however, do not yield an optimum estimate of oxygen tension due to its nonlinear dependence on the ratio of intermediate variables. Moreover, prior knowledge about the variables is very limited so that the level of noise cannot be reliably estimated, thereby affecting the automated choice of the regularization parameter in the RLS method. In this study the problem of optimally estimating oxygen tension in retinal vessels using the maximum a posteriori (MAP) criterion is addressed. For this purpose the conditional distribution of oxygen tension is derived given the phosphorescence lifetime imaging observations and model. The performance of MAP is compared with that of LS and the RLS methods using simulated data and its improved performance in the presence of different levels of noise is demonstrated.
Investigating the Correlational Structures of UV Radiation for Marine Ecosystem due to Ozone Layer Depletion at Pakistan's atmospheric region

European Space Agency (ESA), the World over Scientific organization, North Atlantic Treaty Organization (NATO), National Aeronautics and Space Administration (NASA) and WOrld Meteorological Organization are deeply concerned with the dis-balances caused to a significant extent due to human interference in the natural make-up of earth's ecosystem. In particular, the ozone layer depletion (OLD) over the South Pole is already a serious hazard. The principal long-term effect of OLD appears to be an increase in the UV radiation reaching the earth. In our earlier findings on OLD have been reported and the effects could be observed using the data obtained from a ground based instrument known as Dobson Spectrophotometer installed at GC Quetta Pakistan. We have also computed UV flux reaching Arabian Sea due to the OLD. Then we have presented correlation structures of the population dynamics of marine organisms (fish yield and others) with the intensity of UV flux. The results are compared with the data acquired from Total Ozone Mapping Spectrometer (TOMS) Nimbus -7.
A class of low-noise computationally efficient recursive digital filters with applications to sampling rate alterations

A new structure for multiband recursive digital filters is proposed. For meeting low-pass filter specifications, it uses fewer multiplications than conventional elliptic filter realizations. An approximation to the minimax solution is obtained numerically by minimizing the LP error norm. The analytic optimum for odd order low-pass filters of this new class turns out to be the elliptic filter itself, but in a new configuration. Analytic solution is also obtained for filters used in decimation/interpolation by a factor of 2. There are several realizations for this new structure, the choice of which depends on the location of poles and zeros. Some selected realizations always have low roundoff noise and small limit cycle bounds.