nils-herrmann/clean_scirepeval huggingface dataset:
Fault and intrusion tolerance of wireless sensor networks

The following three questions should be answered in developing new topology with more powerful ability to tolerate node-failure in wireless sensor network. First, what is node-failure tolerance of topologies? Second, how to evaluate this tolerance ability? Third, which type of topologies is more efficient in tolerating node-failure? Without giving the answers, the existing work regards fault-tolerance topology as the multiply connected graph, and use the connectivity of the graph as the standard to evaluate tolerance ability. In this paper, we argue that fault tolerance of topologies is not equivalent to the connectivity of multiply connected graph by illustrating two concrete examples. Then the definition of node-failure tolerance is presented. According fault and intrusion, the two sources of failure nodes, we define fault tolerance and intrusion tolerance as the standards to evaluate the tolerance ability of topologies, and analyze the tolerance performance of hierarchical structure of wireless sensor network by using these standards. Finally, the function relation between hierarchical topology and its tolerance abilities of fault and intrusion is obtained, and an obvious corollary is that fault tolerance increase with the ratio of cluster head hierarchical structure, but with the intrusion tolerance decreasing.
Blind joint equalization and detection in multipath CDMA channel with unknown correlated noise

This paper deals with the problem of blind joint equalisation and detection in a multipath CDMA channel in the presence of multiple-access interference (MAI) and unknown correlated ambient noise. A subspace method for signature waveform estimation is extended to the system with unknown correlated noise. Through signature waveform estimation, a linear MMSE detector can be obtained blindly. Simulation results are provided to show that the linear MMSE detector is near-far resistant.
Watermark embedding in DC components of DCT for binary images

This paper investigates the feasibility of watermark embedding in the discrete cosine transform (DCT) domain for binary images. Watermark embedding is known to be difficult for binary images due to their binary nature. For frequency domain approach to binary image watermarking, a post-embedding binarization is a necessary step to ensure that the watermarked image is still a binary image. This step disturbs the watermark embedded and is likely to remove the watermark. We have succeeded in combating this interference by embedding watermarks in the DC components of DCT and employing a biased binarization threshold. This algorithm can be applied to binary images in general and experiments show that the embedding algorithm proposed can not only survive binarization, but also provide some degree of robustness against common image processing.
Comparative study of some discrete instantaneous frequency estimators

The instantaneous frequency (IF) of a signal is often used as an aid to time-frequency analysis of non-stationary signals. In this paper the comparative study of the optimal discrete IF (DIF) estimator, the Kay-Tretter DIF estimator, the central finite difference (CFD) DIF estimator and the smoothed CFD DIF estimator are presented in the case of a signal distorted by Gaussian white noise. The advantages and disadvantages of these commonly used DIF estimators are concluded.
Performance of antenna diversity reception with correlated Rayleigh fading signals

This paper studies the performance of antenna diversity reception with selection combining (SC) and maximal ratio combining (MRC) in a correlated Rayleigh fading environment. Simple PDF expressions are derived for the output SNR of the SC and MRC diversity receivers. Based on the PDF, the average BERs for DPSK and NCFSK modulation are calculated to show the effects of the correlation. Moreover, the relationship between the average BER and the diversity antennas' separation is given. The presented numerical results show that SC and MRC diversity reception can achieve a good performance only if the distance between two diversity antennas is larger than 0.3/spl lambda/.
Downlink channel covariance matrix (DCCM) estimation for FDD SDMA

The downlink channel covariance matrix (DCCM) is of vital importance in determining downlink beamforming weights for base station (BS) antenna array systems. For frequency-division-duplex (FDD) mode, DCCM is difficult to obtain due to lack of direct measurement of downlink channel responses. A novel technique is proposed for estimating DCCM using uplink channel responses only, which does not need DOA estimation and its association. The new technique is much simpler than the DOA-based approach and the GF algorithm.
Unsupervised Domain Adaptation for Face Anti-Spoofing

Face anti-spoofing (a.k.a. presentation attack detection) has recently emerged as an active topic with great significance for both academia and industry due to the rapidly increasing demand in user authentication on mobile phones, PCs, tablets, and so on. Recently, numerous face spoofing detection schemes have been proposed based on the assumption that training and testing samples are in the same domain in terms of the feature space and marginal probability distribution. However, due to unlimited variations of the dominant conditions (illumination, facial appearance, camera quality, and so on) in face acquisition, such single domain methods lack generalization capability, which further prevents them from being applied in practical applications. In light of this, we introduce an unsupervised domain adaptation face anti-spoofing scheme to address the real-world scenario that learns the classifier for the target domain based on training samples in a different source domain. In particular, an embedding function is first imposed based on source and target domain data, which maps the data to a new space where the distribution similarity can be measured. Subsequently, the Maximum Mean Discrepancy between the latent features in source and target domains is minimized such that a more generalized classifier can be learned. State-of-the-art representations including both hand-crafted and deep neural network learned features are further adopted into the framework to quest the capability of them in domain adaptation. Moreover, we introduce a new database for face spoofing detection, which contains more than 4000 face samples with a large variety of spoofing types, capture devices, illuminations, and so on. Extensive experiments on existing benchmark databases and the new database verify that the proposed approach can gain significantly better generalization capability in cross-domain scenarios by providing consistently better anti-spoofing performance.
Speaker verification for home security system

In this paper, we attempt to develop a reliable speaker verification algorithm that is suitable for use in a home security system. A phoneme-based hidden Markov model (HMM) has been adopted for the task of speaker verification with linear predictive cepstral coefficients (LPCC) as feature vectors for our model. Individual codebooks, designed to enhance performance, are also generated for all speakers in the test database. A simple way of combining the individual phoneme scores for text independent verification is also proposed. An equal error rate (ERR) of 10.5% has been achieved using the best phoneme model and 4.5% when using the combined scores of a 4-phoneme set.
A floating feature detector for handwritten numeral recognition

A novel feature extraction method for handwritten numeral recognition is proposed based on character's geometric structures. A group of stable and reliable global features are defined and extracted. Furthermore, a floating feature detector is proposed to detect and extract tiny segments as fine features. A neural network is employed as the recognisor to conduct experiments on evaluating the feasibility of the new approach. This proposed method demonstrates that the combination of fine features with global features can greatly improve the handwritten character recognition rate compared to those using global features only.
Blind code timing estimation for DS-CDMA signals in unknown colored noise

In previous years, the MUSIC timing estimator were proposed for direct-sequence code-division multiple access (DS-CDMA) signals. It has been generally assumed that the channel noise is temporally white. However it may be invalid in practice due to, for instance, the presence of some narrowband interference or the lumping of secondary users into with the noise. We consider blind code timing in unknown colored noise by using the matrix decomposition technique. A subspace-based code timing estimation algorithm is presented and its performance is evaluated by computer simulation and compared with the MUSIC method. It is proved that the proposed timing estimation algorithm provides better performance than the MUSIC method in colored noise.
An intelligent pressure sensor with self-calibration capability using artificial neural networks

The nonlinear response characteristics of a capacitive pressure sensor (CPS) changes when the ambient temperature changes widely. In such conditions, the calibration becomes difficult, and to obtain an accurate pressure readout, appropriate compensation of the CPS characteristics is needed. We propose an intelligent CPS using artificial neural networks (ANNs) to provide self-calibration and compensation. The proposed ANN model can provide automatic nonlinear compensation and calibration of the CPS characteristics. A microcontroller unit (MCU) based implementation scheme for this model is also considered. Simulation results show that this model can estimate the pressure with a maximum full-scale error of /spl plusmn/1% over a variation of temperature from -50 to 150/spl deg/C.
Pattern-Based Data Hiding for Binary Image Authentication by Connectivity-Preserving

In this paper, a novel blind data hiding method for binary images authentication aims at preserving the connectivity of pixels in a local neighborhood is proposed. The "flippability" of a pixel is determined by imposing three transition criteria in a 3 times 3 moving window centered at the pixel. The "embeddability" of a block is invariant in the watermark embedding process, hence the watermark can be extracted without referring to the original image. The "uneven embeddability" of the host image is handled by embedding the watermark only in those "embeddable" blocks. The locations are chosen in such a way that the visual quality of the watermarked image is guaranteed. Different types of blocks are studied and their abilities to increase the capacity are compared. The problem of how to locate the "embeddable" pixels in a block for different block schemes is addressed which facilitates the incorporation of the cryptographic signature as the hard authenticator watermark to ensure integrity and authenticity of the image. Discussions on the security considerations, visual quality against capacity, counter measures against steganalysis and analysis of the computational load are provided. Comparisons with prior methods show superiority of the proposed scheme
Manipulation Detection on Image Patches Using FusionBoost

In this paper, we propose a novel manipulation detection framework for image patches using a fusion procedure, called FusionBoost, in conjunction with accurately detected derivative correlation features. By first dividing all demosaiced samples of a color image into a number of categories, we estimate their underlying demosaicing formulas based on partial derivative correlation models and extract several types of derivative correlation features. The features are organized into small subsets according to both the demosaicing category and the feature type. For each subset, we train a lightweight manipulation detector using probabilistic support vector machines. FusionBoost is then proposed to learn the weights of an ensemble detector for achieving the minimum error rate. By applying the ensemble detector on cropped photo patches from different image sources, large-scale experiments show that our proposed method achieves low average detection error rates of 2.0% to 4.3% in simultaneously detecting a large variety of common manipulation attempts for image patches from several different source models. Our framework shows good learning efficiency for highly imbalanced tasks. In several patch-based detection examples, we demonstrate the efficacy of the proposed method in detecting image manipulations on local patches.
Time delay estimation at multi-path based on symmetry measure of GCC function

An improved time delay estimator based on the symmetry measurement of the generalised cross-correlation (GCC) function is proposed. At a strong reverberation case, multiple peaks are chosen from the GCC function and the peak with the best symmetry measure is determined as the time delay estimate. Computer simulation results in a room environment for different source locations show the significant improvement on the percentage of anomalies of the time delay estimates against the maximum likelihood TDE (MLTDE) and PHAT methods, it also shows that the proposed time delay estimation is robust to the presence of multi-path propagation, even under a strong reverberation condition and with the source located close to the wall.
Multisampling decision-feedback linear prediction receivers for differential space-time modulation over Rayleigh fast-fading channels

Novel decision-feedback (DF) linear prediction (LP) receivers, which process multiple samples per symbol interval in conjunction with optimal sample combining, are proposed for differential space-time modulation (DSTM) over Rayleigh fast-fading channels. Performance analysis demonstrates that multisampling DF-LP receivers outperform their symbol-rate sampling counterpart in fast fading substantially. In addition, an asymptotically tight upper bound on the pairwise error probability is derived. In view of this bound, the design criterion of DSTM for fast fading is the same as that for block-wise static fading. To avoid the estimation of the second-order statistics of the channel, a polynomial-model-based DF-LP receiver is proposed. It can approach the performance of the optimum DF-LP receiver at high signal-to noise ratios, provided fading is moderate.
Performance analysis of an FFH/BFSK product-combining receiver with multitone jamming over Rician-fading channels

The effects of band multitone jamming (MTJ) and additive white Gaussian noise (AWGN) on the bit-error rate (BER) performance of a fast frequency-hopped binary frequency-shift-keying (FFH/BFSK) product-combining receiver are studied. The FFH system is assumed to experience independent Rician fading channels. The analytical expressions are derived based on the quantization approach which is applicable to higher diversity levels. Numerical results, validated by simulation results, show that system performance is not sensitive to different types of MTJ fading conditions. When the signal amplitude does not experience severe fading, the product-combining receiver is found to be superior to the linear-combining receiver and is only slightly outperformed by the clipper receiver which requires signal side information. However, the converse is true under Rayleigh-fading signal conditions.
RAW tool identification through detected demosaicing regularity

RAW tools are PC software tools that develop the RAWs, i.e. the camera sensor data, into full-color photos. In this paper, we propose to study the internal processing characteristics of these RAW tools using 3 heterogeneous sets of demosaicing features. Through feature-level fusion, normalization and an Eigen-space regularization technique, we derive a compact set of discriminant features. Experimentally, we find that the compact feature set can be used to accurately distinguish 40 RAW-tool classes. A dissimilarity study also shows that the cropped image blocks from different RAW-tool or positional classes have a great deal of dissimilarity in our extracted demosaicing features.
Color space identification from single images

In this paper, we focus on the problem of RGB color space identification from a single image. At the moment, RGB color spaces are widely adopted in photography for image producing. The problems with respect to color space identification, such as to get the consistent printing or displaying quality on screen devices and software applications and prevention of multimedia unauthorized usage(shown or printed by other device via gamut mapping), need to be concerned. Current techniques are all relying on EXchangeable Image File Format (EXIF) to extract color space information. In this paper, we use a two-dimensional non-causal regressive model to explore the image demosaicing properties in order to extract discriminative features and train them on SVM classifier for image color space detection without relying on EXIF. In our experiment, images in three different color spaces (sRGB, adobeRGB and pro PhotoRGB) are generated for color space identification task. The experimental results show that the proposed technique has an good performance on image color space identification.
Two-Layer Binary Image Authentication With Tampering Localization

In this paper, a novel two-layer blind binary image authentication scheme is proposed, in which the first layer is targeted at the overall authentication and the second layer is targeted at identifying the tampering locations. The "flippability" of a pixel is determined by the three "connectivity-preserving" transition criterions described in H. Yang and A.C. Kot, (2005). The image is partitioned into multiple micro-blocks and the micro-blocks are classified into eight categories. The block identifier is defined adaptively for each class and embedded in those "qualified" and "self-detecting" micro-blocks in order to identify the tampered locations. The block identifier is defined in such a way that any changes occurred either to the "qualified" or its neighboring "un-qualified" micro-blocks render the retrieved block identifier different from the one embedded. Experimental results validate the arguments made. Discussions on the accuracy of localization of tamperings are provided
Privacy protection of fingerprint database using lossless data hiding

In this paper, we introduce a fingerprint authentication system for protecting the privacy of the fingerprint template stored in a database. The template, which is a binary fingerprint image after thinning, will be embedded with private personal data in the user enrollment phase. In the user authentication phase, these hidden personal data can be extracted from the stored template for verifying the authenticity of the person who provides the query fingerprint. A novel lossless data hiding scheme is proposed for a thinned fingerprint. By adopting “embeddability criterion”, data is hidden into the template by just adding some boundary pixels in the template. These boundary pixels can be extracted and removed to reconstruct the original thinned fingerprint so that fingerprint matching accuracy is not affected. Compared with using existing binary image data hiding techniques, our scheme has a better performance for a thinned fingerprint.
Lossless data hiding for electronic ink

This paper presents a novel lossless data hiding technique for electronic inks. The proposed algorithm first computes the analytical ink-curve function for each stroke as a set of smoothly concatenated cubic Bezier curves. During embedding, a set of data carrier points on the ink-curve are computed, perturbed and inserted back into the original point array together with some marker points. In extraction, data carrier points are identified and compared with their original positions in order to decode the secret message. Through experiments, we demonstrate that significant mount of secret message can be embedded and the ink-curve computed from the marked ink data closely resembles the original ink-curve.
Text document authentication by integrating inter character and word spaces watermarking

In this paper, a method for watermarking on text document images to authenticate the owner or authorized user is proposed. The proposed method makes use of the integrated inter character and word spaces for watermark embedding. An overlapping component which is of size three is utilized, whereby the relationship of the left and right spaces of the character is employed for the watermark embedding. The integrity of the document can be ensured by comparing the hash value of the character components of the document before and after watermark embedding, which can be applied to other line shifting and word-shifting methods as well. While the authenticity of the document can be ensured by generating the Gold-like sequence, which takes the secret key of the authorized user/owner as the seed value, and it is subsequently XORed with hash value of the character components of the document to generate the content-based watermark. The capacity of the watermark has increased compared with conventional line shifting and word shifting methods. Retrieval of the watermark does not require the presence of the original text document image. Discussions on the cons and pros of the method are also provided. Experimental results are given to validate the arguments
Performance analysis of an FFT-based FFH/BFSK product-combining receiver with partial-band jamming

The performance analysis of a fast frequency-hopped binary frequency-shift-keying (FFH/BFSK) spread-spectrum (SS) system in conjunction with a fast Fourier transform (FFT) processor is presented. Under the conditions of partial-band jamming (PBJ) and additive white Gaussian noise (AWGN), the bit-error rate (BER) expressions of the proposed FFT-based product-combining receiver (PCR) are derived in terms of the characteristic functions of the decision statistics. The BER expressions, validated by simulation results, are applicable to any arbitrary diversity level. The BER results show that there is an optimum diversity level for the FFT-based PCR. The FFT-based PCR is also shown to perform better than the well known square-law linear-combining receiver.
Rejection of multitone jamming in a soft-decision FFH/BFSK linear-combining receiver over a Rician-fading channel

Analytical expressions of bit-error probability are derived for a fast frequency-hopping binary frequency-shift-keying (FFH/BFSK) spread-spectrum (SS) communication system over a fading channel with the worst-case band multitone jamming (MTJ) and additive white Gaussian noise (AWGN). The FFH system employing a linear-combining receiver is investigated. The desired signal and MTJ are assumed to undergo independent fading and our analysis, validated with simulation results, shows that the performance of the system is slightly improved as the severity of the MTJ fading is increased. The worst-case MTJ is shown to be more harmful than the corresponding worst-case partial-band jamming over a fading channel with AWGN.
FFT-based clipper receiver for fast frequency-hopping spread-spectrum system

This paper presents the performance analysis of a binary frequency-shift keying fast frequency-hopping spread-spectrum (BFSK/FFH SS) system equipped with a fast Fourier transform (FFT) processor and a clipper. The FFT-based clipper receiver provides an alternative approach to suppress partial-band jamming (PBJ) in discrete-time domain. The analytical expression for the probability of bit error is obtained and numerical results show that the performance of FFT-based receiver is better than that of the linear-combining receiver and comparable to that of the clipper receiver.