nils-herrmann/clean_scirepeval huggingface dataset:
Jammer suppression in spread spectrum

This paper addresses the performance evaluation of the suppression of a real single tone jammer using singular valve decomposition (SVD). A system model employing the proposed SVD-based technique to reject the single-tone jammer in a direct sequence binary phase shift keying (DS/BPSK) spread spectrum system is presented. The performance of this system is based on the BER results obtained through simulations. The results are shown to compare favorably to those obtained by the conventional two-sided transversal filter method, especially when the jammer power is more dominant.
Image Sharpness Assessment by Sparse Representation

Recent advances in sparse representation show that overcomplete dictionaries learned from natural images can capture high-level features for image analysis. Since atoms in the dictionaries are typically edge patterns and image blur is characterized by the spread of edges, an overcomplete dictionary can be used to measure the extent of blur. Motivated by this, this paper presents a no-reference sparse representation-based image sharpness index. An overcomplete dictionary is first learned using natural images. The blurred image is then represented using the dictionary in a block manner, and block energy is computed using the sparse coefficients. The sharpness score is defined as the variance-normalized energy over a set of selected high-variance blocks, which is achieved by normalizing the total block energy using the sum of block variances. The proposed method is not sensitive to training images, so a universal dictionary can be used to evaluate the sharpness of images. Experiments on six public image quality databases demonstrate the advantages of the proposed method.
A perturbation theory for the analysis of SVD-based algorithms

The problem of statistically analyzing the performance of signal processing algorithms which use the singular value decomposition is adressed in this paper. Such decomposition, which is widely used in system identification and parameter estimation, is a non-linear operation. Consequently, when applied to random data, statistical results are extremely difficult to obtain. The first-order Taylor series expansion is generally used in computing the statistics but the derivative term makes the staistical analysis very difficult. In this paper, a power-like method which results in a simple expression is proposed. The singular vector perturbation using both approaches for the case of low rank approximation is examined.
Image tampering detection by exposing blur type inconsistency

In this paper, we propose a novel method for image tampering detection in multi-type blurred images. After block-based image partitioning, a space-variant prior for local blur kernels is proposed for local blur kernels estimation. Then, the image blocks are clustered using a k-means clustering based on the similarity of local blur kernels to generate blur type invariant regions. Finally, blur types of the regions are classified into out-of-focus or motion blur using a minimum distance classifier. The experimental results show that the proposed method successfully detects and classifies the regions blur types which outperforms the state-of-the-art techniques. Our proposed approach is used to detect inconsistency in the partial blur types of an image as an evidence of image tampering.
A novel hybrid classifier for recognition of handwritten numerals

A hybrid neural network and tree classification system for handwritten numeral recognition is proposed. The recognition system consists of coarse and fine classification based on a variety of stable and reliable global features and local features. For the coarse classifier: a four-layer feedforward neural network with backpropagation learning algorithm is employed to distinguish six subsets {0}, {6}, {8}, , {4,9}, {2,3,5} based on the similarity of character's geometrical features. Three character classes {0}, {6} and {8} are directly recognized from ANN. For each of the last three subsets, a decision tree classifier is built for fine classification as follows: firstly, the specific feature-class relationship is heuristically and empirically created between the feature primitives and corresponding semantic class. Then, an iterative growing and pruning algorithm is used to form a tree classifier. Experiments demonstrated that the proposed hybrid recognition system is robust and flexible, which can achieve a high recognition rate.
Learning Generalized Deep Feature Representation for Face Anti-Spoofing

In this paper, we propose a novel framework leveraging the advantages of the representational ability of deep learning and domain generalization for face spoofing detection. In particular, the generalized deep feature representation is achieved by taking both spatial and temporal information into consideration, and a 3D convolutional neural network architecture tailored for the spatial-temporal input is proposed. The network is first initialized by training with augmented facial samples based on cross-entropy loss and further enhanced with a specifically designed generalization loss, which coherently serves as the regularization term. The training samples from different domains can seamlessly work together for learning the generalized feature representation by manipulating their feature distribution distances. We evaluate the proposed framework with different experimental setups using various databases. Experimental results indicate that our method can learn more discriminative and generalized information compared with the state-of-the-art methods.
A quality measure of mobile phone captured 2D barcode images

2D barcode based mobile tagging technique has not fully matured yet. One of the difficulties is that mobile phone cameras induce inevitable distortions in captured barcode images. For a variety of reasons to be discussed herein, evaluation of the decodability of a barcode image is needed before the decoding process. We propose a novel blurriness measure to classify the samples according to their decodabilities. The proposed quality measure of 2D barcode image is computed by utilizing distinctive histogram features. Experimental results show that the classification accuracy is as high as 94% in our database which consists of samples captured with different phones and conditions. Comparisons with state-of-art quality measures are also conducted. The proposed method is not sensitive to noise, rotation and scaling. It is also applicable to many popular 2D barcode patterns.
New algorithm for multidimensional type-III DCT

New algorithms are proposed for the type-III multidimensional discrete cosine transform (MD-DCT-III). The polynomial transform is used to convert the type-III MD-DCT into a series of one-dimensional type-III discrete cosine transforms (1-D-DCT-III). The algorithms achieve considerable savings on the number of operations compared to the row column method. For computing an r-dimensional DCT-III, the number of multiplications required by the proposed algorithm is only 1//spl tau/ times that needed by the row-column method, and the number of additions is also reduced. Compared to other known fast algorithms for two-dimensional- and MD-DCTs, the proposed method uses about the same number of operations. However, advantages such as better computational structure and flexibility on the choice of dimensional sizes can be achieved.
Differential lattice decoding in noncoherent MIMO

We present improved differential lattice decoding (DLD) for multi-antenna differential modulation by exploiting both basis reduction and sphere decoding. The extra complexity of DLD is shown to be worthwhile in terms of the obtained performance gain over Clarkson et al.'s decoding scheme. With roughly another fold of complexity of basis reduction, DLD augmented by a local search practically attains the maximum-likelihood decoding performance.
Quality guided handbag segmentation

In this paper, we address the problem of handbag segmentation, which is a challenging while important pre-processing for fashion related applications such as handbag tagging and search. Inaccurate segmentation will easily lead to other descriptions of color and shape of the handbag. We first design and extract a set of features for measuring the quality of the handbag segmentation based on some prior knowledge of handbag images. The quality of the handbag segmentation is then measured based on the weighted combination of these features. Guided by such quality measurement, we propose to segment the handbag image by a bottom-up super-pixel fusion. We conduct the experiment on a newly built handbag dataset as well as an existing branded handbag dataset. The results show that our segmentation algorithm performs favorably for handbags. The performance of handbag tagging and recognition is shown to be improved by incorporating such algorithm as pre-processing.
A novel system for fingerprint privacy protection1

This paper proposes a novel system for protecting the fingerprint privacy without using a token or key. In the enrollment, two fingerprints are captured from two of an user's fingers. We extract the minutiae positions from one fingerprint, the orientation from the other fingerprint and the primary cores from both fingerprints. Based on these extracted information, a combined minutiae template is generated and stored in a database. In the authentication, the user needs to provide two query fingerprints from the same two fingers which are used in the enrollment. By storing the combined minutiae template, the complete minutiae feature of a single fingerprint will not be compromised when the database is stolen. Furthermore, because of the similarity in topology, it is also difficult for the attacker to distinguish our template from the minutiae of an original fingerprint. We evaluate the performance of our system over the FVC2002 DB2_A database. The results show that the False Rejection Rate of our system is 3% when the False Acceptance Rate is 0.01%.
Integer DCTs and fast algorithms

A method is proposed to factor the type-II discrete cosine transform (DCT-II) into lifting steps and additions. After approximating the lifting matrices, we get a new type-II integer discrete cosine transform (IntDCT-II) that is float-point multiplication free. Based on the relationships among the various types of DCTs, we can generally factor any DCTs into lifting steps and additions and then get four types of integer DCTs, which need no float-point multiplications. By combining the polynomial transform and the one-dimensional (1-D) integer cosine transform, a two-dimensional (2-D) integer discrete cosine transform is proposed. The proposed transform needs only integer operations and shifts. Furthermore, it is nonseparable and requires a far fewer number of operations than that used by the corresponding row-column 2-D integer discrete cosine transform.
A new efficient group signature scheme based on linear codes

An efficient group signature scheme is presented. The security of this scheme is based on the difficulty of the decoding problem of linear codes, and the security of the ElGamal signature scheme. For a given [n, k, d] code and the corresponding group signature scheme, the lengths of the signatures and the group public key are n+5 and n/sup 2/+5 respectively. With regard to the computational overhead of this scheme, the signing process is basically the same as the ElGamal scheme but with O(n) arithmetic operations; the procedure of opening a signature is very efficient and needs only O(d/sup 2/) arithmetic operations. This scheme allows new members to join a group without modifying the group public key. By distributing new membership certificates to some members, a member can be deleted from a group.
Multiple-antenna differential lattice decoding

From a lattice viewpoint, Clarkson, Sweldens and Zheng significantly reduced the complexity of multiantenna differential decoding. Their approximate decoding algorithm, however, has not unleashed the full potential of lattice decoding. In this paper, we present several improved algorithms, generally referred to as differential lattice decoding (DLD), for multiantenna communication. We first analyze two distinct approximate DLD algorithms, and then develop an algorithm that exactly finds the closest lattice point in the Euclidean space. This exact DLD is subsequently augmented by local search to compensate for the remaining approximation. The small amount of extra complexity of the exact or augmented DLD is rewarded by a clear performance gain. We find that employing basis reduction is very effective to reduce the overall decoding complexity for high lattice dimensions. Moreover, the dimension of the lattice defined in this paper is independent of the number of receive antennas, which results in not only lower complexity, but also better performance for a multiantenna receiver.
Tracking of non-stationary signals with periodic instantaneous frequencies using extended Kalman filters

In situations when the instantaneous frequency (IF) of a non-stationary signal is periodic, estimation of signal parameters by continuous adaptation is expected to provide superior performance to that by block processing approaches as a consequence of the ability to accumulate information over time. The extended Kalman filter is applied to exploit the periodicity of the IF for the tracking of frequency modulation (FM) signals in both the time and frequency domain. No prior knowledge of the IF model is required, and an efficient algorithm for the initialization of filter conditions is presented. Simulation results reveal the outstanding performance of the proposed method in tracking nonstationary signals with IF of different model orders.
Optimization of array geometry for identifiable high resolution parameter estimation in sensor array signal processing

This paper is concerned with the optimization of array geometry using genetic algorithms as the optimization tool. Recent advances in array processing have been focused on developing high resolution algorithms for estimating signal parameters. The problem of optimal design of the array geometry has been neglected and therefore addressed in this paper. An optimal array geometry will correspond to one with the lowest Cramer-Rao bound (CRB) and which gives rise to minimal ambiguities at low SNR. An approach using genetic algorithms (GA) to minimise the CRB, subjected to the ambiguity constraint is proposed and implemented. By utilizing the parallel search capability of the GA, this approach constitutes an efficient design tool for the design of an array of any size and configuration. An alternative using simulated annealing is also proposed. Both approaches are shown to produce optimum array geometries that are superior to the conventional circular array in terms of accuracy and identifiability.
Street-to-shop shoe retrieval with multi-scale viewpoint invariant triplet network

In this paper we aim to find exactly the same shoes given a daily shoe photo (street scenario) that matches the online shop shoe photo (shop scenario). There are large visual differences between the street and shop scenario shoe images. To handle the discrepancy of different scenarios, we learn a feature embedding for shoes via a viewpoint-invariant triplet network, the feature activations of which reflect the inherent similarity between any two shoe images. Specifically, we propose a new loss function that minimizes the distances between images of the same shoes captured from different viewpoints. Moreover, we train the proposed triplet network at two different scales so that the representation of shoes incorporates different levels of invariance at different scales. To support training the multi-scale triplet networks, we collect a large dataset with shoe images from the daily life and online shopping websites. Experiments on the dataset show excellence over state-of-the-art approaches, which demonstrate the effectiveness of our proposed method.
Chebyschev functional link artificial neural networks for nonlinear dynamic system identification

An alternative novel artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The main drawback of feedforward neural networks such as a multi-layer perceptron (MLP) trained with backpropagation (BP) algorithm is that it requires a large amount of computation and the rate of error convergence is slow. The proposed Chebyschev functional link ANN (C-FLANN) is found to have much less computational requirement and its performance is found to be superior to that of a MLP for the complex task of nonlinear dynamic system identification, even in the case of additive input noise to the system.
DOA estimation of speech source with microphone arrays

In this paper, we address the problem of DOA estimation for speech signal with a linear equal space microphone array. Based on the speech signal spectrum, a frequency selective wideband DOA method is proposed without a priori knowledge about the source location. Resampling, spatial interpolation and least square technique are employed in the proposed DOA estimation for the far field and near field cases. Computer simulations results show that the proposed method can be used to estimate the DOA of the speech signal at a low signal to noise ratio and with strong interferences and reverberations.
Error probabilities of an FFH/BFSK self-normalizing receiver in a Rician fading channel with multitone jamming

We derive the analytical bit-error rate (BER) expressions for a fast frequency-hopped binary frequency-shift keying self-normalizing receiver over a fading channel with the worst-case band multitone jamming (MTJ) and additive white Gaussian noise (AWGN). The desired signal and MTJ are assumed to undergo independent Rician fading and our analyses, validated with simulation results, show that the system performance is not sensitive to different types of MTJ fading conditions. The self-normalizing receiver is found to be superior to the linear-combining receiver when the signal amplitude does not experience severe fading, while the converse is true under Rayleigh fading signal conditions. Under a Rician fading channel and AWGN conditions, the worst-case MTJ and the worst-case partial-band noise jamming are shown to have similar effects on the BER performance of the self-normalizing receiver with diversity.
Keynote speaker I: Cell analysis and assembly by micro and nano robotics system

Cell system synthesis and assembly have been more important together with the single cell analysis, so that it is expected to build the 3 D biological structure by micro-nano robotics system. Micro-nano robotic system nowadays has a solid discipline, as synergetic integration of the micro and nano sensor, actuator, control, computer and material, and wide spread applications to industry and consumer in our daily life. Micro-nano fabrication, materials, assembly with evaluation leads downsizing of the products and give more economical material and energy efficiency, and more functions from the viewpoints of Green and Life innovations. This micro-nano robotic system can also show to give a new dimension of theory and applications in the life science, such as medical engineering, bio-engineering, bio-robotics and other areas. In particular, more active observation with micro-nano robotic manipulation has been more popular, leading new discovery and finding in single cell analysis as well as improving the quality of life. Then it is challenging to investigate how we can construct a 3 D cell assembly by the synthesis approach based on the knowledge and findings of the cell analysis. There will be several methods to achieve this challenge. In this paper, we show some of the unique assembly methods by the DEP, hydro gel fibers and so on.
Sparsity based reflection removal using external patch search

Reflection removal aims at separating the mixture of the desired background scenes and the undesired reflections, when the photos are taken through the glass. It has both aesthetic and practical applications which can largely improve the performance of many multimedia tasks. Existing reflection removal approaches heavily rely on scene priors such as separable sparse gradients brought by different levels of blur, and they easily fail when such priors are not observed in many real scenes. Sparse representation models and nonlocal image priors have shown their effectiveness in image restoration with self similarity. In this work, we propose a reflection removal method benefited from the sparsity and nonlocal image prior as a unified optimization framework. We leverage the retrieved image patch from an external database to overcome the limited prior information in the input mixture image and self similarity search. The experimental results show that our proposed model performs better than the existing state-of-the-art reflection removal method for both objective and subjective image qualities.
Error probabilities of an FFT-based FFH/BFSK linear-combining receiver with partial-band noise jamming and AWGN

This paper presents the performance analysis of a fast frequency-hopped binary frequency-shift-keying (FFH/BFSK) linear-combining receiver in conjunction with a fast Fourier transform (FFT) processor. Under the conditions of partial-band noise jamming (PBNJ) and additive white Gaussian noise (AWGN), the bit-error rate (BER) expressions of the proposed FFT-based linear-combining receiver 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 proposed FFT-based receiver provides an alternative to the conventional matched-filter receiver. The FFT-based receiver is shown to give comparable performance with the corresponding matched-filter receiver. The worst-case PBNJ fraction is shown to be inversely proportional to the signal-to-jamming ratio and the performance of the FFT-based linear-combining receiver is degraded as the diversity level is increased.
The statistical performance of state-variable balancing and Prony's method in parameter estimation

This paper presents a statistical analysis of the state-variable balancing and Prony methods for estimating the parameters of exponential signals in the presence of additive noise. The case of frequency estimation for a single sinusoid is carried out in detail. Analytical expressions for the variances of the frequency estimates at high signal-to-noise ratios are derived. The calculated variances are compared to the Cramer-Rao bound. The results are validated by simulations over a wide range of signal-to-noise ratios. The analysis and simulations show that the state-variable balancing method can provide slightly more accurate frequency estimates while avoiding the problem of selecting the signal zeros of the Prony polynomial.
Performance study of time delay estimation in a room environment [microphone arrays]

Time delay estimation (TDE) is often used for source localization in microphone arrays and the performance of TDE is always affected by the reverberation in a room. In this paper, the performance of TDE in a rectangular room environment is studied for microphone arrays. The probability of anomalies of time delay estimates is obtained theoretically for the multi-path propagation in the room. The effects of the room reverberation on the TDE under different microphone patterns, microphone position configurations and spacings between the microphones are evaluated.