nils-herrmann/clean_scirepeval huggingface dataset:
A hybrid approach for salt dome detection in 2D and 3D seismic data

Salt bodies play an important role in subsurface geology, therefore accurate salt dome detection is essential for any seismic interpretation task. Detecting salt body boundary and shape accurately, however, is very difficult due to large noise and amplitude variations in seismic data. Due to the limitations of manual picking, automatic segmentation algorithms are preferred to locate salt domes within the seismic images. In this work, we propose a robust salt dome detection technique based on combined edge and texture attributes. The proposed algorithm overcomes the drawbacks of existing texture attributes based salt dome detection techniques which are heavily dependent upon the relevance of attributes to the geological nature of salt domes, and the number of attributes used for classification. The combination of edge based and texture based attributes ensures that the proposed algorithm works well even if the salt boundary is represented only by a weak reflector. We tested the proposed algorithm on the Netherlands offshore F3 block. Our experimental results show that the proposed algorithm can detect salt boundaries with high accuracy superior to existing gradient based as well as texture based techniques when used separately.
Fingerprint feature extraction using block-direction on reconstructed images

Fingerprints have been used as unique identifiers of individuals for a very long time. As fingerprint databases are characterized by their large size and may contain noisy and distorted images, an efficient representation of the images is essential for a reliable identification. Considering fingerprints as sample images from non-stationary processes with flow patterns, we propose here a robust technique to extract their features. The unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their features. The ridges are extracted from enhanced foreground areas based on local dominant ridge directions. The resulting bit-mapped images are thinned and smoothed to detect structural features. A large number of false features are eliminated in the proposed post-processing stage. The proposed algorithm results in an efficient and fast representation of fingerprints which accurately retains the fidelity in minutiae.
A hybrid information maximisation (HIM) algorithm for optimal feature selection from multi-channel data

A novel feature selection algorithm is derived for multi-channel data. This algorithm is a hybrid information maximisation (HIM) technique based on (1) maximising the mutual information between the input and output of a network using the infomax algorithm proposed by Linsker (1988), and (2) maximising the mutual information between outputs of different network modules using the Imax algorithm introduced by Becker (see Network Computation in Neural Systems, vol.7, p.7-31, 1996). The infomax algorithm is useful in reducing the redundancy in the output units, while the Imax algorithm is capable of selecting higher order features from the input units. In this paper, we analyse the two methods and generalise the learning procedure of the Imax algorithm to make it suitable for maximising the mutual information between multi-dimensional output units from different network modules contrary to the original Imax algorithm which only maximises mutual information between two output units. We show that the proposed HIM algorithm provides a better representation of the input compared to the original two algorithms when used separately. Finally, the HIM is evaluated with respect to biological plausibility in the case of feature selection from two-channel EEG data.
A new approach to very low-rate speech coding using temporal decomposition

Temporal decomposition (TD) is a method to reduce the correlation between speech spectral parameter sets using orthogonalization. The resulting parameters (so-called weightings or target vectors) correspond, mostly, to least-dependent phonetic states, which produce the desired sounds. Other phonetic states can be interpreted as transition states which force the event functions to overlap. On the basis of this assumption, we propose to approximate the event functions by sample functions to focus on the phonetically important states, and to use the resulting parameters for constructing a very low-rate speech coder. The objective and subjective evaluation of synthesized speech, mostly from the intelligibility view point, confirm the assumptions made on the events.
A new approach to efficient interpolative determination of pitch contour using temporal decomposition

A new technique to predict the pitch contour by using a small number of pitch values is proposed in this paper. The technique is based on the correlation between phonetic evolution and pitch variations during voiced speech. To follow the phonetic evolution, "temporal decomposition" (TD) is used which detects "event" functions, as interpolation paths, and their locations (centroids), as the certain instants to determine the pitch values. By using the proposed technique, the pitch contour is predicted with an overall error of less than 5% with some spectral parameter sets used in TD. Also, we show that this technique can give a better result than that of conventional frame by frame pitch detection methods, on the basis of a perceptually-based spectral distance measure of reconstructed speech.
Combined coding of audio and speech signals using LPC and the discrete wavelet transform

The aim of combined coding is to compress a mixed signal consisting of speech and music to the highest possible compression ratio and yet maintain user acceptability. This paper proposes a hybrid coder which is capable of coding combined speech and audio signals sampled over the range 16 kHz to 32 kHz. The coder provides high quality audio coding at competitive bit rates over this range of signal sampling frequencies and offers further coding gains when used with speech signals alone.
On the performance of adaptive steerable antenna arrays in communication applications

The performance of a steerable beam adaptive array is affected by a number of factors including the signal to noise ratio of the desired input, other undesired signals, the number and spacing between sensor elements, the feedback loop gain of the array, and the reliability of prior knowledge on the direction of the desired signal. This paper provides a detailed derivation and a comprehensive discussion on the effect of such factors on the overall performance of steerable adaptive arrays.
High quality audio coding using multipulse LPC and wavelet decomposition

Most current work in the area of high quality audio coding falls under one of two categories: transform or sub-band coding. LPC coders since based on modelling human voice production systems are found to be inappropriate in modelling music and other non-speech sounds. A more improved model for such signals is shown to be the multipulse LPC model. In this paper we propose to improve the quality of the multipulse model by first passing the signal of interest through a filter bank and then extracting the multipulse parameters from each of the bandpass filter outputs. The idea of the wavelet decomposition is utilised for the design of the filter bank. Both the multipulse model and the wavelet decomposition are well known. But a combination of both has not been exploited yet. This combination is expected to lead to a new way in high quality low bit rate audio coding.
Modelling mammographic images using fractional Brownian motion

Developing models for microcalcification clusters remains difficult because of the variety of shapes and orientations, background tissue structures and image qualities. A model which has been applied successfully to medical images is fractional Brownian motion. The fractal dimension, scale-invariance and self-similarity properties of fractional Brownian motion closely resemble many natural phenomena such as growth patterns. These properties are investigated and several fractal dimension estimation algorithms are presented for both 1-D and 2-D signals. A selection of these estimators is applied to the problem of malignant and benign microcalcification cluster discrimination. Results indicate that modelling mammographic images with fractional Brownian motion can provide superior indicators of the global texture than measures based on traditional texture features.
Polynomial order reducing property of lattice filters in detection of linear FM signals

Order-reduction techniques have already been successfully applied to the detection of linear FM signals in white noise. In this paper, reflection coefficients of adaptive lattice filters are used, in a new aspect, to reduce the order of input polynomial phase FM signals. This concept is then used to detect chirp signals in white noise. To define an appropriate hypothesis problem for the transformed signals in the first adaptive reflection coefficient, its statistical behaviour is experimentally investigated. It is shown that for small confidence intervals, the relevant probability density function for the transformed signal can be assumed Gaussian.
Speech decomposition using the discrete cosine transform, and adaptive thresholding

We present a new algorithm, named DCT-HN, for decomposing speech into two parts-a harmonic and a noise component. The technique uses adaptive thresholding in the discrete cosine transform (DCT) domain to determine the noise level for each frame. The technique is iterative in nature but converges quickly to a local optimum. The method can be applied to either speech or residual signals. Experimental results for both synthetic and real speech signals showed that DCT-HN yields the same results as a similar decomposition method, referred to as YAD, for high levels of signal to noise ratio (SNR). However, the proposed technique outperforms the most available methods for low SNRs.
Preliminary investigation into the use of the cross Wigner-Ville distribution to detect high impedance earth faults in power systems

This paper presents the results of a preliminary investigation into the viability of using the cross Wigner-Ville distribution (XWVD) in the detection of high impedance earth faults in power systems. High impedance earth faults are characterised an extremely low current with a time varying spectral characteristic superimposed on the mains current. It is shown that the XWVD provides a good identification of the extremely small high impedance arc fault current with signal to noise ratios as low as -10dB.
A new algorithm for multi-channel EEG signal analysis using mutual information

Electroencephalogram (EEG) signals have long been used for the analysis of brain activities and for the detection of abnormalities (such as seizures). More recently, and with advance of computer technology, we have seen new applications using EEG signals in the control of PC keyboards through BCIs (Brain Computer Interfaces). These EEG signals are normally collected through multi-sensors (8,12, or 16 channels). For proper interpretation of such data, several techniques have been proposed to extract features from the collected multi-channel data, then analyse them, or classify them into patterns. However, most existing techniques do not take into consideration the inherent relationship among features across channels. Here, we propose a scheme based on a hybrid information maximization concept (HIM) to process multi-channel data for optimal feature extraction. The experiments carried show a clear advantage of the approach over principal component and canonical correlation analysis.
A new algorithm for EEG feature selection using mutual information

An EEG feature selection technique for the purpose of classification is developed. The technique selects those features that have maximum mutual information with the specified classes of interest (two classes in this case). Obviously, the simplest way is to consider all possible feature subsets (M out of N). However, even with a small number of features, this procedure is computationally impossible and can not be used in practice. Given the fact that most features used to represent the EEG signal are sets of features (such as AR parameters), our technique considers a trade off between computational cost and chosen feature combination. This contrasts other techniques which select features individually. The classification accuracy of features obtained by applying our technique outperforms those obtained by applying individual feature selection methods when applied to EEG signals.
A novel fingerprint image compression technique using the wavelet transform and piecewise-uniform pyramid lattice vector quantisation

A novel compression algorithm for fingerprint images is introduced. Using wavelet packets and lattice vector quantisation, a new vector quantisation scheme based on an accurate model for the distribution of the wavelet coefficients is presented. In the new algorithm, no assumptions are made about the lattice parameters and no training and multi-quantising are required. The proposed algorithms achieve the best rate-distortion performance by adapting to the statistical characteristics of the source image in each sub-image. Compared to other available image compression algorithms, the proposed algorithms result in higher quality reconstructed images for identical bit rates.
Recognition of Arabic License Plates using NN

License plate recognition (LPR) systems are a key to many traffic related applications such as road traffic monitoring or parking lots access control. We propose an automatic license plate recognition system for GCC license plates. The system presents an algorithm for the extraction of license plate and recognition of Arabic characters and numerals. Preliminary work on the system has been investigated on real images of vehicles captured under various illumination conditions. Real time LPR plays a major role in automatic monitoring of traffic rules and maintaining law enforcement on public roads. The automatic identification of vehicles by the contents of their license plates is important in private transport applications.
A Dempster-Shafer theory of evidence approach for combining trained neural networks

The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since there is not a unique way to perform such a combination, we have developed an algorithm which adapts to the training data set so that the overall mean square error is minimised. The proposed method was proved to be superior and more robust than other available combination methods.
A novel scalable audio coder based on warped linear prediction and the wavelet transform

The paper presents a novel bitstream scalable audio coder. In the proposed coder, the full bandwidth of input audio is first split into two. A hybrid WLPC-wavelet representation is used to encode the low frequency components (<; 11 kHz). In this method, the excitation to the WLPC synthesis filter is decomposed into subbands using a wavelet filterbank, and perceptually encoded. Two stage quantisation of the wavelet coefficients is used to provide scalability. The high frequency components of the input are assumed to be noisy, and efficiently encoded using an LPC noise model. The output bitstream can be decoded at rates between 16 kbps and 70 kbps. As the bitrate increases, so too does the signal quality. At 70 kbps, the quality is near transparent. At the intermediate rates, the coder gives comparable performance to the MPEG layer III coder, when the MPEG coder operates at a similar (but fixed) bitrate.
A multi-classifier image based vacant parking detection system

With the rapid expansion of major cities, the tremendous increase in the number of vehicles, and the construction of huge buildings and parking areas, there is a need to develop smart parking systems to assist drivers finding the nearest available parking spots. Such systems have witnessed substantial research efforts in developed countries. There are mainly four categories of car parking management systems: counter-based, wired-sensor-based, wireless-sensor-based, and image-based. In this paper, we develop, implement, and test an image-based system for the detection of vacant spaces in a parking area. Following an initial edge detection stage, we combine edge density, closed contour density, and foreground/background pixel ratio, at every car parking spot, to identify whether a car is present or not. Combining the features above results in a robust vacant space detection system at low computational cost.
The comprehensive ICP (CICP) algorithm and its application to the multidmoal registration of 3D surfaces of the heart

Image registration is a valuable technique for medical diagnosis and treatment. In this paper, we present an enhanced implementation of the popular iterative closest point (ICP) algorithm developed for the registration of 3D free-form closed surfaces, based on the use of a look up matrix for finding the best correspondence pairs. The algorithm, called comprehensive ICP (CICP) algorithm, is then successfully applied for comparing two sequences of 3D surfaces of the left ventricle of the heart, obtained from two different modalities: 4D Echography and Gated SPECT. The results show a good correspondence between the reconstructed sequence from 4D echography and the reference gated SPECT sequence. This study can be extended to comparing different medical reconstructions of sphere-like shaped organs collected from different modalities.
Scale-Space Properties of the Multiscale Morphological Dilation-Erosion

A multiscale morphological dilation-erosion smoothing operation and its associated scale-space expansion for multidimensional signals are proposed. Properties of this smoothing operation are developed and, in particular a scale-space monotonic property for signal extrema is demonstrated. Scale-space fingerprints from this approach have advantages over Gaussian scale-space fingerprints in that: they are defined for negative values of the scale parameter; have monotonic properties in two and higher dimensions; do not cause features to be shifted by the smoothing; and allow efficient computation. The application of reduced multiscale dilation-erosion fingerprints to the surface matching of terrain is demonstrated.
Parameter estimation for filtered discrete fractal signals

The problems of estimating the parameters of discrete fractals and filtered discrete fractals directions are considered. A maximum likelihood approach for estimating the parameters of pure fractals is presented. In particular, it is shown that the computation of the likelihood function does not require any matrix inversion as previously thought. For filtered fractals, an iterative procedure is proposed to estimate the parameters of the fractal signal and those of the shaping filter. The procedure has been shown to converge experimentally.<<ETX>>
Space curve recognition based on the wavelet transform and string-matching techniques

A technique for representing and recognising 3-D or space curves is presented. In the proposed algorithm, the space curves are represented by a set of two zero-crossing representations which are constructed based on the dyadic wavelet transform. These representations are then described in the form of an ordered set of complex numbers which is referred to as the compact representation of the space curves. A string-matching technique is adapted for comparing two curves using their compact representations. Experimental results show that the proposed technique can be used for recognising space curves under similarity transformation with and without additive noise.
Wireless Sensor Networks for Pilgrims Tracking

Every year, and for five days, about three million pilgrims gather in the small city of Makkah, Saudi Arabia, to perform the rituals of Hajj (Pilgrimage). Tracking the movement of such a large number of people is crucial to the pilgrims themselves and the authorities managing the whole event. This letter reports a real-time pilgrim tracking system that has been designed and implemented. The system relies on a dedicated delay-tolerant wireless sensor network (WSN). This WSN is interfaced to the Internet through gateway(s) available from an internet service provider (ISP). Energy efficiency, robustness, and reliability are key factors in the design of the system. Each pilgrim is given a mobile sensor unit which includes a GPS chip, a microcontroller, and antennas. A network of fixed units is installed in the Holy area for receiving and forwarding data. Periodically, each mobile unit sends its user identification (UID), latitude, longitude, and a time stamp. A central server maps the latitude and longitude information on a geographical information system (GIS). The developed system can be used to track a specific or a group of pilgrims. The developed system was tested during the last two pilgrim seasons. The pilot system was able to successfully track all pilgrims who participated in the experiment.
Arabic Sign Language Recognition an Image-Based Approach

In this paper we propose an image based system for Arabic sign language recognition. A Gaussian skin color model is used to detect the signer's face. The centroid of the detected face is then used as a reference to track the hands movement using region growing from the sequence of images comprising the signs. A number of features are then selected from the detected hand regions across the sequence of images. The recognition stage is performed using a hidden Markov model. The proposed system achieved a recognition accuracy of about 93% for a data set of 300 signs with leave one out method.
Morphological scale-space fingerprints and their use in object recognition in range images

We present the theory of multiscale dilation-erosion scale-space and the process of feature extraction via morphological scale-space fingerprints. We then discuss the reduced form of the fingerprints and state the scale-space causality theorem. These fingerprints are then applied to the recognition of multiple objects from range data. The proposed recognition system is invariant to translation, rotation, scale, and partial occlusion. We demonstrate results for the recognition of human faces in a scene, and the recognition of mountain features in a digital elevation map.<<ETX>>
A high impedance fault detector using a neural network and subband decomposition

High impedance faults (HIFs) are not easily detectable using conventional overcurrent protection relays. The fault current for HIF is usually less than the normal load current, thus the overcurrent relays cannot easily distinguish HIFs from normal currents. A new method based on a subband decomposition of the current is presented. The energies from the different subbands are used as input to train an artificial neural network (ANN) for the detection of HIFs. The technique, not only detects HIF faults, but also classifies the signals into one of several classes. The main advantage of this method is that it is less sensitive to noise and HIF can be distinguished from similar events, even in the presence of high levels of noise.
Combined source channel decoding for image transmission over fading channels

Previous studies highlighted the importance of using residual redundancy techniques in the design of a joint source channel decoder for the case of data transmission over binary symmetrical channel. This paper uses the sequence maximum a posteriori (MAP) method, which is one of the residual redundancy techniques to design a joint source channel coding with the representation of the wireless communication channel by the Gilbert-Elliott channel (GEC). The paper also discusses the issue of parameters estimation for the GEC channel. A new modification is suggested to the Baum-Welch re-estimation technique in order to improve its performance and reduces its computational requirements.