nils-herrmann/clean_scirepeval huggingface dataset:
Efficient IIR and FIR fan filters

A procedure for designing computationally efficient FIR and IIR fan filters is developed. The fan filters are derived from one-dimensional prototypes which are used in changing sampling rates by a factor of two. The transfer function B(z) of the prototype is expressed as B(z) = T_1(z^2)+zT_2(z^2) , which leads to a simple expression for the fan filter transfer function. The relation between the passband and stopband deviation requirements of the prototype and the fan filter is derived and employed in the design for meeting two commonly given sets of specifications. A special class of elliptic filters with zeros on the imaginary axis in the z plane is shown to yield computationally efficient IIR fan filters. FIR half-band filters are shown to be suitable one-dimensional prototypes for FIR fan filter design. In this case, symmetry constraints on FIR fan filter impulse response are automatically satisfied.
OFDMA nonlinear distortion recovery using improved clipping localization and iterative projections

In OFDM-based systems the effect of nonlinear amplification at the transmitter is increasingly being tackled by receiver-oriented methods that aim to reduce distortion through estimating and recovering the clipped signal. However, in OFDMA systems, recovery of clipped signals poses new challenges because not all modulation information about other users is available to any individual user. Recently a scheme based on simple clip localization and band limited signal recovery has been proposed to address this problem but its performance is limited. In this paper, we propose to improve clip location estimation by exploiting key factors such as closeness of signal level to clipping threshold, clip time-span, and out-of-band power for local windowed subsequences, and then incorporate clip location estimates in iterative projection method to recover the clipped signal. Extensive simulations demonstrate that the performance is significantly improved with the proposed scheme compared with existing methods.
Retinal oxygen tension estimation in phosphorescence lifetime images using regularized least squares

The role of inadequate retinal oxygenation in the development of retinal diseases is an important problem for investigation. In our study, quantitative measurement of oxygen tension in retinal vasculatures is made with the noninvasive technique of phosphorescence lifetime imaging. A frequency domain approach is used to estimate the fluorescence or phosphorescence lifetime by regression analysis. Measurements are influenced by the presence of noise due to which least squares estimation yields results with high variance. Since accurate estimates are important in the study, we propose a spatial regularization method by adding a quadratic penalty term in the cost function instead of directly using classical least squares. The effectiveness of the proposed method is demonstrated by applying it to experimental and simulated data to show improved performance when compared with unregularized estimation.
Kernel particle filter: iterative sampling for efficient visual tracking

Particle filter has recently received attention in computer vision applications due to attributes such as its ability to carry multiple hypotheses and its relaxation of the linearity assumption. Its shortcoming is increase in complexity with state dimension. We present kernel particle filter as a variation of particle filter with improved sampling efficiency and performance in visual tracking. Unlike existing methods that use stochastic or deterministic optimization procedures to find the modes in a likelihood function, we redistribute particles by invoking kernel-based representation of densities and introducing mean shift as an iterative mode-seeking procedure, in which particles move towards dominant modes while still maintaining as fair samples from the posterior. Experiments on face and limb tracking show that the algorithm is superior to conventional particle filter in handling weak dynamic models and occlusions with 60% fewer particles in 3-9 dimensional spaces.
Robust tracking of cyclic nonrigid motion

Cyclic motion underlies several human activities including exercising, running, and walking. Accurate tracking of such motion in video data helps in developing computer-aided applications such as gait analysis, person identification, patient rehabilitation, etc. This paper presents a set of novel techniques for tracking cyclic human motion based on decomposing complex cyclic motion into simpler motion components and introducing phase coupling between the components. The intensity of coupling is adaptively adjusted during tracking such that a strong coupling is triggered when self-occlusion occurs. In our experiments we use sequential Monte Carlo methods for tracking a walking human. We show that this adaptive phase coupling of component motions handles occlusion and self-occlusion with significantly improved accuracy while avoiding the limitations caused by a poorly trained dynamic model.
IIR discrete-time Hilbert transformers

A procedure for obtaining IIR Hilbert transformers from generalized halfband filters  is described. The solution is shown to be suited for specifications where the "care"-band for the Hilbert transformers is centered at 0.5π. Even when this condition is not satisfied, the proposed Hilbert transformers obtained by overdesign may often be preferable for implementation to those obtained from a general design procedure . Analytic solutions for noncausal Hilbert transformers and 90° phase shifters can be derived from special classes of classical low-pass filters. The design procedure described by Gold et al.  is shown to be equivalent to applying a transformation on Hilbert transformers derived from elliptic low-pass filters. Numerical approximation can be used for designing IIR Hilbert transformers with a right-sided impulse response. This gives us a single causal IIR filter (instead of a pair of 90° phase shifters) whose output is the approximate Hilbert transform of the delayed version of the input.
Low-complexity implementation of non-subsampled directional filter banks using polyphase representations and generalized separable processing

In this paper we present efficient filtering methods for the directional decomposition of images and extraction of directional cues. The design and implementation framework proposed in our paper is based on a non-subsampled tree structure, which allows for redundancy capturing some image characteristics for detection. We examine structures for analysis using 2M filters, M > 3, with narrow wedge-shaped passbands. Our structures are derived from the decimation of two-dimensional impulse responses composed of configurations of one-dimensional building blocks. In the implementation, we reconfigure transfer functions of the filters with decimated impulse responses by exploiting appropriate polyphase structure of one-dimensional building blocks to retain the property of generalized separability along directions determined by the passband orientations of the directional filters. The reconfiguration depends on the value of M, and in this paper we provide general rules for choosing the integer factors for polyphase decomposition that help re-cast the expressions for the transfer functions of the analysis bank of filters into forms that allow efficient implementation. The proposed implementation is shown to provide significant improvement compared with conventional two-dimensional implementation. Simulations show that the filtering structure provides good frequency selectivity on airborne surveillance images.
Improved Peak Windowing for PAPR Reduction in OFDM

The large peak to average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) transmission translates to system performance degradation due to low power efficiency in the presence of nonlinear power amplification. To reduce PAPR, many OFDM systems have adopted the peak windowing method. However, existing peak windowing schemes suffer from performance limitations especially in the presence of multiple closely-spaced peaks. In this paper, two new peak windowing schemes are proposed to improve performance by taking care of closely-spaced peaks. Simulation results show that the two proposed schemes outperform existing schemes in attaining significantly lower out-of-band radiation given the same in-band distortion constraints.
Inverse filter approach to pitch modification: application to concatenative synthesis of female speech

A new method for modifying the pitch of units of recorded female speech is described. This method was developed to overcome limitations in an otherwise promising technique called residual-excited linear prediction (RELP). In the new method, the stored speech unit is processed with a suitably shaped time-varying filter. The filtered signal is modified according to the required change in the fundamental frequency. The modified filtered signal is applied to the inverse of the above-mentioned prefilter. Based on observations of spectra of multiple recordings of the same speech unit at different pitch frequencies, the magnitude response of the inverse filter was chosen to have a significantly less peaky structure than that which is typically obtained in LPC. Speech modifications using this method were found to be superior in quality to those obtained by RELP, while at the same time being less sensitive than RELP to changes in pitch marking.
Power-efficient re-gridding architecture for accelerating Non-uniform Fast Fourier Transform

This paper proposes a novel FPGA-based accelerator for the memory and compute-intense re-gridding process used in computation of Non-uniform Fast Fourier Transform (NuFFT). The re-gridding process interpolates arbitrary sampled data onto a uniform grid using an interpolation kernel function. This regridding step is considered one of the most time consuming step in entire NuFFT computation. We propose a memory-efficient technique based on the novel use of customizable hardware components such as FPGA block memory in First-In-First-Out (FIFO) configuration, fill-rate based arbiter, distributed RAM and an array of pipelined single precision floating point multipliers and adders. The proposed architecture exhibits high performance over a wide range of configurations and data-sizes. A speed-up of over 9.6 was achieved when compared with existing FPGA-based technique at a 7 times higher MFLOPS per watt. Compared to GPU based technique, over 6 times higher MFLOPS per watts were achieved.
Relative effects of resolution and quantization on the quality of compressed medical images

Medical image scanners produce digitized information at different spatial resolutions, and this affects the perceived image quality after image compression. Quality of decompressed medical images is assessed by the extent to which diagnostic information is affected by compression, and the medical interpretation requires participation of medical experts in observer studies. We undertook a study to investigate the effect of resolution from digitization on medical image compression, in the context of enabling image transmission using low to moderate bandwidth telecommunication facilities. The test material consisted of a series of adult chest radiographs containing multiple subtle abnormalities. In this paper, the effects of sampling resolution and frequency component quantization on image quality are compared, and issues for further investigation in general image compression are discussed.<<ETX>>
Improved Image Retrieval for Efficient Localization in Urban Areas Using Location Uncertainty Data

Accurate localization of mobile devices based on camera-acquired visual media information usually requires a search over a very large GPS-referenced image database. This paper proposes an efficient method for limiting the search space for image retrieval engine by extracting and leveraging additional media information about Estimated Positional Error (EPE) to address complexity and accuracy issues in the search, especially to be used for compensating GPS location inaccuracy in dense urban areas. To test our procedure we have created a database by acquiring Google Street View (GSV) images and set of query images along with their EPE tag for down town of Chicago. Experimental results demonstrate how our proposed method can improve performance just by utilizing a data that is available for mobile systems such as smart phones.
A multimedia-content authentication and recovery protocol in peer-to-peer networks

In peer-to-peer (P2P) and overlay network environments, digital multimedia is allowed to be freely copied, shared and disseminated in a decentralized manner. Such environments do not guarantee authentication or preservation of the data. Furthermore, if the multimedia data, particularly video, images, or audio, has been polluted during this sharing process, there are no decentralized mechanisms that guarantee recovery of the original data. We propose a new distributed and highly scalable authentication and recovery protocol to achieve timely authentication and recovery in polluted P2P networks. We characterize and model the recovery problem in the polluted P2P environment. To optimize the decentralized authentication and recovery operations, we introduce three collaboration models: one-peer full-file, one-peer multiple-chunk and cluster-based. We evaluate the performance of the three models and highlight the effect of file-service capacity assuming zipfpsilas file popularity distribution. We also compare our models with a recovery model that is equivalent of LOCKSS preservation system for text documents. Performance evaluation results are presented to demonstrate the effectiveness of the proposed protocol.
A multiplication-free framework for signal processing and applications in biomedical image analysis

A new framework for signal processing is introduced based on a novel vector product definition that permits a multiplier-free implementation. First a new product of two real numbers is defined as the sum of their absolute values, with the sign determined by product of the hard-limited numbers. This new product of real numbers is used to define a similar product of vectors in RN. The new vector product of two identical vectors reduces to a scaled version of the l1 norm of the vector. The main advantage of this framework is that it yields multiplication-free computationally efficient algorithms for performing some important tasks in signal processing. An application to the problem of cancer cell line image classification is presented that uses the notion of a co-difference matrix that is analogous to a covariance matrix except that the vector products are based on our new proposed framework. Results show the effectiveness of this approach when the proposed co-difference matrix is compared with a covariance matrix.
Compressed-image authentication using compensated signature embedding

In this paper, an effective content-based authentication system with localization is proposed for compressed images. The system is capable of authenticating images published in a compressed format by tolerating distortion introduced due to both embedding and compression while detecting other manipulations. Towards this end, a content-based fragile signature is derived and embedded into a compressed image using a robust watermarking technique. The scheme incorporates a mechanism to overcome the alteration of the fragile signature caused by the embedding and compression distortions. We build a general framework for the authentication based on the new concept of compensated signature embedding, such that media samples are iteratively adjusted to compensate for the distortion introduced and to restore the signature. The scheme was then customized for image data compressed using the JPEG2000 standard and tested after subjecting the compressed image to added degradations. Simulation results are presented to show that the proposed scheme successfully performs the authentication. The scheme does not require any third party reference or side information. The signature and embedding are designed to reveal tamper localization. The scheme can be modified to work with other types of compression methods and embedding schemes.
Eye tracking using Markov models

We propose an eye detection and tracking method based on color and geometrical features of the human face using a monocular camera. In this method, a decision is made on whether the eyes are closed or not and, using a Markov chain framework to model temporal evolution, the subject's gaze is determined. The method can successfully track facial features even while the head assumes various poses, so long as the nostrils are visible to the camera. We compare our method with recently proposed techniques and results show that it provides more accurate tracking and robustness to variations in view of the face. A procedure for detecting tracking errors is employed to recover the loss of feature points in case of occlusion or very fast head movement. The method may be used in monitoring a driver's alertness and detecting drowsiness, and also in applications requiring non-contact human computer interaction.
Spatio-Temporal Hierarchical Data Aggregation Using Compressive Sensing (ST-HDACS)

The problem of power-efficient data aggregation in wireless sensor networks (WSNs) using Compressive Sensing (CS) to reduce the amount of data communicated is addressed here. Existing CS-based data aggregation methods can be categorized as either those that apply CS spatially to minimize the amount of data to be communicated in the routing path, or those that seek to minimize the amount of data by applying CS temporally at each sensor. A recently reported scheme that is described as a Spatial-Temporal CS scheme randomly selects a subset of data but does not apply compression in the routing path. Here we formulate a spatial-temporal data collection model in WSNs and refer to it as Spatial-Temporal Hierarchical Data Aggregation using Compressive Sensing (ST-HDACS). The idea underlying ST-HDACS consists of two key components: Firstly, for each time snapshot of data collected in the network, a subset of nodes is randomly selected and designated for data sensing and transmission. A power-efficient Adaptive Hierarchical Data Aggregation (A-HDACS) scheme is incorporated in our work to compress the spatial data to be communicated in the routing path. Secondly, after performing data collection over a designated time period, a Matrix Completion (MC) problem is executed in the fusion center to recover the data for the entire network over the full data collection period. The performance of the proposed method is evaluated and it is demonstrated that ST-HDACS scheme reduces the amount of data for transmission and improves the associated energy consumption more effectively than existing CS-based data aggregation schemes.
Unitary precoding and power control in MIMO systems with limited feedback

Spatial multiplexing (SM) proves effective in increasing data rate in a narrowband multiple-input multiple-output (MIMO) system. Past work has shown that unitary precoding with limited feedback can be used at the transmitter to improve the bit error rate (BER) performance. Further performance gain can be achieved if power control is used along with unitary preceding at the transmitter. In this paper we propose a novel power control scheme called modified spatial-temporal truncated channel inversion (MST-TCI) for limited feedback MIMO systems in block Rayleigh fading channels. A low-complexity algorithm called codebook optimum index loading (COIL) is proposed to generate near-optimum codebooks for quantizing MST-TCI power control information. Simulation results show that the proposed scheme significantly outperforms the limited feedback unitary precoding scheme
Efficient tracking of cyclic human motion by component motion

A set of techniques are presented for Bayesian tracking of cyclic human motion based on decomposing a complex cyclic motion into component motions. Phases of the component motions are defined and two different mechanisms for coupling the phases are described: importance sampling and an observation model. The intensity of coupling is adaptively adjusted during tracking such that strong coupling is triggered during self-occlusion. Tracking of a walking human using motion decomposition and phase coupling is performed with an improved particle filter called the approximate kernel particle filter. We show that our approach handles foreign object occlusion and self-occlusion with improved accuracy and efficiency compared with conventional tracking without decomposition.
Low-complexity EPS scheme for PAPR reduction in OFDM with no transmission of side information

High peak-to-average power ratio (PAPR) is one of the major drawbacks of orthogonal frequency division multiplexing (OFDM). In  a low-complexity probabilistic scheme to reduce PAPR in OFDM, called erasure pattern selection (EPS), was proposed. The EPS scheme described in  requires the transmission of side information with proper coding. In this paper we address waveform design for OFDM transmission and investigate a method that avoids transmission of side information. Simulation results show that the probability of incorrect detection of the selected erasure pattern is smaller than 10-3 for SNR larger than 6 dB. This means that only 1 in 1000 symbols have to be retransmitted. Also, EPS provide a reduction in PAPR of about 1.5 dB.
Blind Detection for Additive Embedding Using Underdetermined ICA

This paper presents an efficient blind watermark detection scheme for additive embedding (AE) based on under-determined independent component analysis (ICA) framework. The proposed detector assumes that the host signal and the watermark obey non-Gaussian distributions and watermark embedding follows AE model. The proposed blind watermark detector employs blind source separation (BSS) for underdetermined mixtures for watermark estimation. Simulation results are presented showing that the proposed detector performs significantly better than existing correlation based blind detectors operating without suppressing the host signal interference at the detector
The use of geometric diversity for spectral dominance in underground imaging

Underground imaging of dielectric and conductive anomalies performed using ground penetrating radars (GPRs) requires expensive wideband systems to increase the resolution. The advent of tomographic principles in multi-monostatic GPRs dramatically improved the imaging capabilities and suggested the possibility of reducing the bandwidth of the probing waveform. In this work we propose to extend the tomographic principles to the case of below-ground distributed sensing, thus taking advantage of the geometric diversity. We show that, by using geometric diversity, the frequency content required to image below-ground targets is drastically reduced to virtually a single monochromatic signal, thus achieving full spectral dominance in the waveform design.
Kernel particle filter for visual tracking

A new particle filter-the Kernel Particle Filter (KPF)-is proposed for visual tracking in image sequences. The KPF invokes kernels to form a continuous estimate of the posterior density function. Particles are allocated based on the gradient information estimated from the kernel density estimate of the posterior. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident in scenarios of small system noise or weak dynamic models where the standard particle filter usually fails.