nils-herrmann/clean_scirepeval huggingface dataset:
Joint Statistics of Radio Frequency Interference in Multiantenna Receivers

Many wireless data communications systems, such as LTE, Wi-Fi, and Wimax, have become or are rapidly becoming interference limited due to radio frequency interference (RFI) generated by both human-made and natural sources. Human-made sources of RFI include uncoordinated devices operating in the same frequency band, devices communicating in adjacent frequency bands, and computational platform subsystems radiating clock frequencies and their harmonics. Additive RFI for these wireless systems has predominantly non-Gaussian statistics, and is well modeled by the Middleton Class A distribution for centralized networks, and the symmetric alpha stable distribution for decentralized networks. Our primary contribution is the derivation of joint spatial statistical models of RFI generated from uncoordinated interfering sources randomly distributed around a multiantenna receiver. The derivation is based on statistical-physical interference generation and propagation mechanisms. Prior results on joint statistics of multiantenna interference model either spatially independent or spatially isotropic interference, and do not provide a statistical-physical derivation for certain network environments. Our proposed joint spatial statistical model captures a continuum between spatially independent and spatially isotropic statistics, and hence includes many previous results as special cases. Practical applications include co-channel interference modeling for various wireless network environments, including wireless ad hoc, cellular, local area, and femtocell networks.
Real-time foveation techniques for H.263 video encoding in software

Video coding techniques employ characteristics of the human visual system (HVS) to achieve high coding efficiency. Lee (2000) and Bovik have exploited foveation, which is a non-uniform resolution representation of an image reflecting the sampling in the retina, for low bit-rate video coding. We develop a fast approximation of the foveation model and demonstrate real-time foveation techniques in the spatial domain and discrete cosine transform (DCT) domain. We incorporate fast DCT domain foveation into the baseline H.263 video encoding standard. We show that DCT-domain foveation requires much lower computational overhead but generates higher bit rates than spatial domain foveation. Our techniques do not require any modifications of the decoder.
Long range channel prediction for adaptive OFDM systems

In this paper, different techniques for long-range channel prediction for OFDM systems are investigated. Frequency domain channel prediction on each OFDM data subcarrier is first explored and it is shown that the optimum prediction filter depends only on the time-domain channel statistics for the wide-sense stationary uncorrelated scattering (WSSUS) wireless channel. Frequency domain prediction on the pilot subcarriers is investigated next, where the optimum prediction filter is determined for each pilot subcarrier and is reused for all the nearby data subcarriers. Finally, time-domain channel prediction on the multipath taps is explored. It is shown that frequency domain prediction on the pilot subcarriers performs almost identically to prediction using all subcarriers. Furthermore, it is also shown that time-domain prediction outperforms the frequency domain prediction methods.
Nondestructive inspection using Compton scatter tomography

A method for non-invasively generating tomographic images of electron density in materials using Compton scattered gamma rays is investigated. Electron density is an indicator of density or composition changes in a material. In Compton scatter tomography, the energy distribution of monoenergetic gamma rays is measured after scattering from a material target. Measuring a scattered gamma ray's energy localizes the scattering position to a definable region of the sample. We develop an analytic computational model to study the image quality of a realistic system. In particular, the deleterious effects of finite source and detector size, Compton broadening, and Poisson noise are investigated. A backprojection algorithm is presented to demonstrate the impact of these effects on image reconstruction and contrast recovery. Detection of corrosion in low-Z materials is an application of interest, and is used to study the Compton scatter tomography technique.
Optimizing LBP Structure For Visual Recognition Using Binary Quadratic Programming

Local binary pattern (LBP) and its variants have shown promising results in visual recognition applications. However, most existing approaches rely on a pre-defined structure to extract LBP features. We argue that the optimal LBP structure should be task-dependent and propose a new method to learn discriminative LBP structures. We formulate it as a point selection problem: Given a set of point candidates, the goal is to select an optimal subset to compose the LBP structure. In view of the problems of current feature selection algorithms, we propose a novel Maximal Joint Mutual Information criterion. Then, the point selection is converted into a binary quadratic programming problem and solved efficiently via the branch and bound algorithm. The proposed LBP structures demonstrate superior performance to the state-of-the-art approaches on classifying both spatial patterns in scene recognition and spatial-temporal patterns in dynamic texture recognition.
Weighted Iterative Truncated Mean Filter

The iterative truncated arithmetic mean (ITM) filter was proposed recently. It offers a way to estimate the sample median by simple arithmetic computing instead of the time consuming data sorting. In this paper, a rich class of filters named weighted ITM (WITM) filters are proposed. By iteratively truncating the extreme samples, the output of the WITM filter converges to the weighted median. Proper stopping criterion makes the WITM filters own merits of both the weighted mean and median filters and hence outperforms the both in some applications. Three structures are designed to enable the WITM filters being low-, band- and high-pass filters. Properties of these filters are presented and analyzed. Experimental evaluations are carried out on both synthesis and real data to verify some properties of the WITM filters.
Dynamic texture recognition using enhanced LBP features

This paper addresses the challenge of recognizing dynamic textures based on spatial-temporal descriptors. Dynamic textures are composed of both spatial and temporal features. The histogram of local binary pattern (LBP) has been used in dynamic texture recognition. However, its performance is limited by the reliability issues of the LBP histograms. In this paper, two learning-based approaches are proposed to remove the unreliable information in LBP features by utilizing Principal Histogram Analysis. Furthermore, a super histogram is proposed to improve the reliability of the LBP histograms. The temporal information is partially transferred to the super histogram. The proposed approaches are evaluated on two widely used benchmark databases: UCLA and Dyntex++ databases. Superior performance is demonstrated compared with the state of the arts.
Fingerprint Identification with Exclusive and Continuous Classification

Fingerprint identification from large database is a great challenge as both the accuracy and the speed of one-to-many matching may deteriorate significantly comparing to verification where only one-to-one matching is needed. This paper proposes a combined classification approach, which takes advantages of exclusive and continuous classification by performing the continuous classification after the exclusive classification. To circumvent the problems of small number of classes in the traditional exclusive classification, a clustering technique is proposed that exploits the similarities among the database templates and partitions them into much larger number of non-overlapping clusters than the conventional exclusive classification. Clustering technique serves as a coarse level classification which speeds up the followed continuous classification. Experimental results on NIST-4 fingerprint database demonstrate that the proposed approach outperforms various existing approaches in terms of not only the classification accuracy but also the classification efficiency
Corrections to “Common-Mode Cancellation in Sinusoidal Gating With Balanced InGaAs/InP Single Photon Avalanche Diodes” [Dec 12 1505-1511]

In the above paper (ibid., vol. 48, no. 12, pp. 1505-1511, Dec. 2012), the authors appeared in the wrong order. They should have appeared as follows: Zhiwen Lu, Wenlu Sun, Joe C. Campbell, Fellow, IEEE, Xudong Jiang, and Mark A. Itzler, Fellow,
Iris recognition on edge maps

Iris recognition is a potential tool in secure personal identification and authentication due to its desirable properties such as uniqueness, non-invasiveness and stability of human iris patterns. In this paper, a new approach based on the Hausdorff distance measure is proposed for iris recognition. A new measure, namely local partial Hausdorff, is computed directly on the binary edge features of the normalized iris images. The proposed edge dissimilarity measure is tested on a set of well- segmented UPOL iris images. For different values of parameters such as block size and partialness, the results indicate high recognition performance of the proposed method.
SIFT features for face recognition

Scale Invariant Feature Transform (SIFT) has shown to be very powerful for general object detection/recognition. And recently, it has been applied in face recognition. However, the original SIFT algorithm may not be optimal for analyzing face images. In this paper, we analyze the performance of SIFT and study its deficiencies when applied to face recognition. We propose two new approaches: Keypoints-Preserving-SIFT (KPSIFT) which keeps all the initial keypoints as features and Partial-Descriptor-SIFT (PDSIFT) where keypoints detected at large scale and near face boundaries are described by a partial descriptor. Furthermore, we compare the performances of holistic approaches: Fisherface (FLDA), the null space approach (NLDA) and Eigenfeature Regularization and Extraction (ERE) with feature based approaches: SIFT, KPSIFT and PDSIFT. Experimental results on ORL and AR databases show that our proposed approaches KPSIFT and PDSIFT can achieve better performance than the original SIFT. Moreover, the performance of PDSIFT is significantly better than FLDA and NLDA. And PDSIFT can achieve the same or better performance than the most successful holistic approach ERE.
Active Area Uniformity of InGaAs/InP Single-Photon Avalanche Diodes

We present a detailed characterization of the active area uniformity of InGaAs/InP Single-Photon Avalanche Diodes (SPADs) from two different design iterations. Nonuniformity of the electric field within the device active area has been measured through 2-D scans of detection efficiency and timing response to a pulsed laser. Additionally, we measured the near-infrared luminescence emitted by hot carriers during the avalanche. The nonuniformity is stronger at lower excess bias, with much higher and nonuniform electric field at the edge of the active area than expected, and it is fainter at higher excess bias, due to the saturation of the avalanche triggering efficiency. The main drawbacks are that the detection efficiency is position dependent when the SPAD is not fiber pigtailed and that the temporal response is worse, because of the nonuniform delay in the avalanche build-up across the SPAD active area.
Face alignment based on the multi-scale local features

Many face recognition algorithms depend on careful positioning of face images into the same canonical pose. Currently, this positioning is usually done by detecting the locations of eyes. And the face images are transformed to the same positions according to the eye coordinates detected. In this paper, we describe a method based on multi-scale local features to achieve face alignment automatically not just dependent on the localizations of two eyes. Given an unaligned face image resulting from a face detector and a set of aligned face images in the data set, we build an automatic transformation mechanism, under which the unaligned face image can be precisely aligned for the following recognition process. Our alignment method improves performance on face recognition tasks, over images aligned by many other algorithms.
Face recognition using sift features

Scale Invariant Feature Transform (SIFT) has shown to be a powerful technique for general object recognition/detection. In this paper, we propose two new approaches: Volume-SIFT (VSIFT) and Partial-Descriptor-SIFT (PDSIFT) for face recognition based on the original SIFT algorithm. We compare holistic approaches: Fisherface (FLDA), the null space approach (NLDA) and Eigenfeature Regularization and Extraction (ERE) with feature based approaches: SIFT and PDSIFT. Experiments on the ORL and AR databases show that the performance of PDSIFT is significantly better than the original SIFT approach. Moreover, PDSIFT can achieve comparable performance as the most successful holistic approach ERE and significantly outperforms FLDA and NLDA.
Discovering Class-Specific Spatial Layouts for Scene Recognition

Scene image is a spatial composition of objects and background contexts and finding discriminative spatial layouts is critical for scene recognition. In this letter, we propose an $\ell _1$ -regularized max-margin formulation to discover class-specific spatial layouts by jointly learning the image classifier and the class-specific spatial layouts for scene recognition. Unlike previous methods that classify images into different categories either without considering the spatial layouts explicitly or only using class-generic spatial layout, our proposed method can discover a sparse combination of class-specific spatial layouts for different scenes and boost the recognition performance. Experiments on scene-15, landuse-21, and MIT indoor-67 datasets validate the advantages of our proposed algorithm.
Accurate Cervical Cell Segmentation from Overlapping Clumps in Pap Smear Images

Accurate segmentation of cervical cells in Pap smear images is an important step in automatic pre-cancer identification in the uterine cervix. One of the major segmentation challenges is overlapping of cytoplasm, which has not been well-addressed in previous studies. To tackle the overlapping issue, this paper proposes a learning-based method with robust shape priors to segment individual cell in Pap smear images to support automatic monitoring of changes in cells, which is a vital prerequisite of early detection of cervical cancer. We define this splitting problem as a discrete labeling task for multiple cells with a suitable cost function. The labeling results are then fed into our dynamic multi-template deformation model for further boundary refinement. Multi-scale deep convolutional networks are adopted to learn the diverse cell appearance features. We also incorporated high-level shape information to guide segmentation where cell boundary might be weak or lost due to cell overlapping. An evaluation carried out using two different datasets demonstrates the superiority of our proposed method over the state-of-the-art methods in terms of segmentation accuracy.
Knowledge guided adaptive binarization for 2D barcode images captured by mobile phones

In this paper, we investigate the problem of binarization of the 2D barcode images captured by mobile devices. The poor quality of the images due to noise, nonuniform illumination and inherent limitation and distortion of the camera makes the task of binarization more challenging. The global thresholding techniques employed by most existing approaches do not work well for the barcode images captured under uncontrolled conditions. Hence, we propose an adaptive binarization technique by taking the prior knowledge such as the edge structure and maximum element width of the 2D barcode into consideration. Incrementally updating the elements in a moving window is proposed to expediate the process for mobile phone applications. Comparisons with other binarization methods showthe superior performance of our method, which achieves about 96.6% recognition rate on a dataset of 787 images.
High Capacity Data Hiding for Binary Images in Morphological Wavelet Transform Domain

This paper investigates the problem of data hiding for binary images authentication in morphological binary wavelet transform domain. Directly using the detail coefficients as a location map to determine the data hiding locations is difficult to achieve blind watermark extraction. Hence, we view flipping an edge pixel as shifting the edge location one pixel horizontally and vertically. Based on this observation, we propose an interlaced transform to track the shifted edges. Different from existing block-based approach, in which the block size is constrained by not less than 3 x 3, we process the image in 2 x 2 blocks. The two processing cases that the flippantly condition of one is not affected by flipping the candidates of another are combined such that an extremely large capacity can be achieved, which slightly sacrifices the visual quality.
Noise-Resistant Local Binary Pattern With an Embedded Error-Correction Mechanism

Local binary pattern (LBP) is sensitive to noise. Local ternary pattern (LTP) partially solves this problem. Both LBP and LTP, however, treat the corrupted image patterns as they are. In view of this, we propose a noise-resistant LBP (NRLBP) to preserve the image local structures in presence of noise. The small pixel difference is vulnerable to noise. Thus, we encode it as an uncertain state first, and then determine its value based on the other bits of the LBP code. It is widely accepted that most of the image local structures are represented by uniform codes and noise patterns most likely fall into the non-uniform codes. Therefore, we assign the value of an uncertain bit hence as to form possible uniform codes. Thus, we develop an error-correction mechanism to recover the distorted image patterns. In addition, we find that some image patterns such as lines are not captured in uniform codes. Those line patterns may appear less frequently than uniform codes, but they represent a set of important local primitives for pattern recognition. Thus, we propose an extended noise-resistant LBP (ENRLBP) to capture line patterns. The proposed NRLBP and ENRLBP are more resistant to noise compared with LBP, LTP, and many other variants. On various applications, the proposed NRLBP and ENRLBP demonstrate superior performance to LBP/LTP variants.
Asymmetric Principal Component and Discriminant Analyses for Pattern Classification

This paper studies the roles of the principal component and discriminant analyses in the pattern classification and explores their problems with the asymmetric classes and/or the unbalanced training data. An asymmetric principal component analysis (APCA) is proposed to remove the unreliable dimensions more effectively than the conventional PCA. Targeted at the two-class problem, an asymmetric discriminant analysis in the APCA subspace is proposed to regularize the eigenvalue that is, in general, a biased estimate of the variance in the corresponding dimension. These efforts facilitate a reliable and discriminative feature extraction for the asymmetric classes and/or the unbalanced training data. The proposed approach is validated in the experiments by comparing it with the related methods. It consistently achieves the highest classification accuracy among all tested methods in the experiments.
Human detection using Discriminative and Robust Local Binary Pattern

Despite superior performance of Local Binary Pattern (LBP) in texture classification and face detection, its performance in human detection has been limited for two reasons. Firstly, LBP differentiates a bright human from a dark background and vice-versa. This increases the intra-class variation of humans. Secondly, LBP is contrast and illumination invariant. It does not discriminate between weak contrast local regions and similar strong contrast ones, resulting in a similar feature representation. Non-Redundant LBP (NRLBP) has been proposed to solve the first issue of LBP. However, an inherent limitation of NRLBP is that LBP codes and their complements in the same block are mapped to the same code. Furthermore, NRLBP, like LBP, is also contrast and illumination invariant. In this paper, we propose a novel edge-texture feature, Discriminative Robust Local Binary Pattern (DRLBP), for human detection. DRLBP alleviates the problems of LBP and NRLBP by considering the weighted sum and absolute difference of a LBP code and its complement. Our experimental results show that DRLBP consistently outperforms LBP and NRLBP for human detection.
Multi-Scale Feature Extraction for Face Recognition

Face recognition has been a very active research area in the past two decades. Many attempts have been made to understand the process how human beings perceive human faces. It is widely accepted that face recognition may rely on both componential cues (such as eyes, mouth, nose, cheeks) and non-componential/holistic information (the spatial relations between these features), though how these cues should be optimally integrated remains unclear. In this paper, we present a new different observer's view approach using multi-scale feature extraction from face images. The basic idea of the proposed method is to construct facial features from multi-scale image patches from different face components and then employ a subspace PCA method for further dimensionality reduction and good representation of facial features. Finally, combining the contributions of each component features draws the recognition decision. 2,388 frontal face images of FERET face database are used for evaluating the proposed method and results are encouraging
An effective color space for face recognition

The three color components specifying a color can be defined in various ways leading to significantly different classification abilities. Several effective color spaces including RQCr, DCS and ZRG have been proposed to achieve better face recognition performance. However, their performance is not consistent on different databases. What's more, the framework of effective color spaces has not been thoroughly studied yet. In this paper, we propose an effective color space LC\C2 based on a framework of effective color spaces. LC\C2 consists of one discriminant luminance component L and two discriminant chrominance components C\C2. To find the discriminant luminance component, 4 luminance components from existing effective color models are compared. After that, the weighted color space normalization technique (WCSN) is applied on the DCS color space to generate two complementary and discriminative chrominance components. Experiments conducted on three databases (FRGC, AR and CMU Multi-PIE) show that the proposed color space LC\C2 achieves the best face recognition performance consistently.
Afterpulsing Effects in Free-Running InGaAsP Single-Photon Avalanche Diodes

We demonstrate large-area (80 mum diameter) InP-based single-photon avalanche diodes for Geiger-mode operation at 1.06 mum with dark count rates of ~1000 Hz at high detection efficiencies of 30% at 237 K, as well as simulations of dark count rate and detection efficiency that provide good agreement with measured data. Experimental results obtained using free-running operation illustrate the strong impact of afterpulsing effects for short (~200 ns) hold-off times. We present an analysis of these free-running results that quantifies the contribution of afterpulsing to the total count rate.