nils-herrmann/clean_scirepeval huggingface dataset:
Fingerprint feature enhancement using block-direction on reconstructed images

Fingerprints have been used as unique identifiers of individuals for a very long time. The identification of fingerprint (FP) images is based on matching the features of a query FP, against those stored in a database. As FP databases are characterized by their large size and may contain noisy and distorted query images, an efficient and robust representation of FP images is essential for a reliable identification. Assuming the FPs to be sample images from non-stationary processes of textured images of flow patterns, we propose a new technique for preprocessing FP images for the purpose of identification. In the proposed algorithm, the enhancement as well as the ridge extraction processes are based on local dominant ridge directions. The obtained thinned image is then smoothed using morphological operations to detect the FP structural features. The proposed algorithm results in an efficient, robust and fast representation of FPs which accurately retains the fidelity in minutiae (ridge endings and bifurcations).
A memory-assisted lossless compression algorithm for medical images

Rapid growth of emerging medical applications such as e-health and tele-medicine requires fast, low cost, and often lossless access to massive amount of medical images and data over bandlimited channels. In this paper, we first show that significant amount of correlation and redundancy exist across different medical images. Such a correlation can be utilized to achieve better compression, and consequently less storage and less communication overhead on the network. We propose a novel memory-assisted compression technique, as a learning-based universal coding, which can be used to complement any existing algorithm to further eliminate redundancies across images. The approach is motivated by the fact that, often in medical applications, massive amount of correlated images from the same family are available as training data for learning the dependencies and deriving appropriate reference models. Such models can then be used for compression of any new image from the same family. In particular, Principal Component Analysis (PCA) is applied on a set of images from training data to form the required reference models. The proposed memory-assisted compression allows each image to be processed independently of other images, and hence allows individual image access and transmission. Experimental results on X-ray images show that the proposed algorithm achieves 20% improvement over and above traditional lossless image compression methods reported in the literature.
Optimal feature selection using information maximisation: case of biomedical data

The hybrid information maximisation (HIM) algorithm is derived. This algorithm is based on maximising the mutual information (MI) between the input and output of a network using the infomax principle, and between outputs of different network modules using the Imax algorithm. These two folds enable reducing the redundancy in output units in addition to selecting higher order features from input units. We analyse the proposed algorithm and generalise the learning procedure of the Imax algorithm. We show that the proposed HIM algorithm provides a better representation of input compared to the original two algorithms when used separately. An example showing the power of the HIM algorithm in the analysis of EEG data is discussed.
Arabie sign language recognition using the Microsoft Kinect

Several studies have been carried on sign language recognition systems, however, practically deployable system for real-time use is still a challenge. Traditionally, sign language recognition systems have either used sensor gloves or digital cameras to acquire and process hand gestures. Both approaches exhibit some disadvantages for real time deployment that hindered it large scale adoption. With the growth witnessed in gaming systems, two new instruments have been introduced namely, the Microsoft Kinect (MK) and the leap motion controller. The MK system has been developed to interact with video games by tracking full body movements and gestures. To overcome some of the disadvantages of the classical methods, we propose here to develop an Arabic sign language recognition system based on MK system. The developed system was tested with 20 signs from the Arabic sign language dictionary. Therefore, in this paper, we present our experiment carried out using the MK setup on 20 Arabic sign language words. Video samples of both true color images and depth images were collected from native deaf signer. Linear Discriminant analysis was used for feature dimension reduction and sign classification. Furthermore, fusion from RGB and depth sensor was carried at feature and decision level giving an overall best accuracy of 99.8%.
Dual LMCs fusion for recognition of isolated Arabic sign language words

In this paper, we propose a Dual-Leap Motion Controllers (DLMC) based Arabic Sign Language recognition system. More particularly, we propose to use both front and side controllers to cater for the challenges of finger occlusions and missing data. For feature extraction, we select an optimum set of geometric features extracted from both controllers, while for classification, we used both a Bayesian approach with a Gaussian Mixture Model (GMM) and a simple Linear Discriminant Analysis (LDA) approach. Though this paper focused only on the GMM approach. Data was collected from a native adult signer, for 100 isolated Arabic words. Ten observations were collected for each of the signs. The proposed framework uses an intelligent strategy to handle the case of missing data from one or both controllers. A recognition accuracy of 94.63% was achieved, with the proposed system. The proposed system outperforms glove-based systems and a single-LMC based techniques.
Preprocessing noisy EEG data using time-frequency peak filtering

This paper considers the problem of spectral parameter estimation for electroencephalogram (EEG) data in the presence of white Gaussian noise. A comparison of three known spectral estimation techniques is first presented. The methods work well at high signalto-noise ratio (SNR). However a t low SNR, which often characterises the EEG, these methods fail to produce good spectral estimates. Here, we discuss a new preprocessing technique for filtering noisy data. This technique is based on time-frequency peak filtering. Experimental results show that this method results in improved spectral estimates.
A Noise Constrained Least Mean Fourth Adaptive Algorithm

In this work, a noise-constrained least mean fourth (NCLMF) adaptive algorithm is proposed. Based on the fact that in many practical applications an accurate estimate of the measurement noise variance is available, or can be easily estimated, the learning speed of the LMF algorithm can be then increased considerably by adding a constraint to it. This noise constrained LMF algorithm can be seen as a variable step-size LMF algorithm. The main aim of this paper is to derive the NCLMF adaptive algorithm, analyze its convergence behaviour, and assess its performance in different noise environments. Moreover, the concept of energy conservation is used to carry out the rigorous steady-state analysis. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and as expected, improved performance is obtained through the use of this technique over the traditional LMF algorithm.
Unveiling the Hidden Assumptions of Energy Detector Based Spectrum Sensing for Cognitive Radios

Cognitive radio is a promising solution to current problem of spectrum scarcity. It relies on efficient spectrum sensing. Energy detection is the most dominantly used spectrum sensing approach owing to its low computational complexity and ability to identify spectrum holes without requiring a priori knowledge of primary transmission characteristics. This paper offers a comprehensive tutorial on energy detection based spectrum sensing and presents an in depth analysis of the test statistic for energy detector. General structure of the test statistic and corresponding threshold are presented to address existing ambiguities in the literature. The derivation of exact distribution of the test statistic, reported in the literature, is revisited and hidden assumptions on the primary user signal model are unveiled. In addition, the scope of detection probability results is discussed for identifying various classes of random primary transmissions. Gaussian approximations of the test statistic are investigated. Specifically, the roles of signal to noise ratio and performance constraint in terms of probability of detection or false alarm are highlighted when Normal approximations are used in place of exact expressions.
Wideband audio compression using a combined wavelet and WLPC representation

In this paper, we present results for a combined wavelet warped linear prediction (WLP) audio coder. In contrast to conventional LP, WLP allows for the control of frequency resolution to closely match the response of the human auditory system. The coder first uses WLP analysis on each frame of audio, and then applies a discrete wavelet transform (DWT) to the residual signal (prediction error). A psychoacoustic model is used in parallel to obtain a global masking threshold used in bit allocation. Bits are dynamically allocated to the DWT coefficients in an attempt to minimise the perceptually significant quantisation error. For monophonic signals sampled at 44.1 kHz, the coder achieves near transparent quality for a variety of speech and music signals at an average bit-rate of 64 kb/s. The power of the proposed coder resides in its easy scalability to lower bit rates.
A comparative study of spectral estimation techniques for noisy non-stationary signals with application to EEG data

The paper considers the problem of spectral estimation of noisy non-stationary signals with application to electroencephalogram (EEG) data. Four well known methods for estimating the time-varying spectrum of a non-stationary signal are first reviewed and their performance compared. These methods which work well when the signal-to-noise ratio (SNR) is high, are shown to fail with varying degrees as SNR decreases. A technique for preprocessing noisy EEG data called time-frequency peak filtering (TFPF) is then presented and used to process EEG signals whose spectral content are highly non-stationary and difficult to model. It is shown that marked improvement in spectral estimates result after using the TFPF method.<<ETX>>
Bearing fault diagnosis using wavelet analysis

The development of real-time fault detection and identification systems will allow a migration from expensive scheduled based maintenance to the more efficient, less costly alternatives of condition based maintenance. This paper presents a detailed analysis of different methods using wavelet analysis in the diagnosis of faults in rotating machinery from its mechanical vibrations. More specifically, the discrete wavelet transform and the discrete wavelet packet transform will be discussed, as well as the choice of the mother wavelet and the different features that can be extracted from such transforms.
Fingerprint compression using a modified wavelet transform and pyramid lattice vector quantization

A new compression algorithm for fingerprint images is introduced. A modified wavelet packet scheme which uses a fixed decomposition structure, matched to the statistics of fingerprint images, is presented. A technique for determining the most important coefficients is introduced. The algorithm uses both hard and soft thresholding schemes to make the procedure fast and efficient. The bit allocation for each subimage of the modified coefficients is determined. Each subimage uses a different quantization technique based on its entropy. Then, a lossless compression technique, Huffman, is used to obtain further compression. The algorithm results in a high compression ratio and a high reconstructed image quality with a low computational cost, compared to other existing algorithms. The performance of the proposed algorithm is compared to that of other decomposition techniques: ordinary wavelet transform (OWT), entropy-based best basis selection (E-BBB), wavelet/scalar quantization (WSQ) and JPEG.
A novel algorithm for low bit rate speech compression using a hybrid LP-harmonics model

We present a new LP-harmonic speech codec. At the coder speech signal is pre-processed, and an LP analysis is performed, together with pitch estimation and voicing decision. At the decoder and when the frame is voiced, the encoded parameters are used to estimate the spectrum envelope, extract and classify the harmonics as either strong or weak depending on their relative distance from multiples of the fundamental frequency. Strong harmonics parameters are then used to generate pure sinusoids. While weak harmonics are used to generate a mixed signal of a pure sinusoid and a random-like signal. For unvoiced frames, the excitation of the LP filter is generated as a white noise signal. The proposed model allows for the mixing of strong and weak periodic signals together with random signals to produce an excitation input that results in natural speech. Informal testing of the coder working at 1.82 kb/s showed that the output speech has high intelligibility, with quality comparable to that of a 4 kb/s sinusoidal codec.
An algorithm for reducing the effect of compression/decompression techniques on fmgerprint minutiae

We show in this paper that the wavelet transform is an effective technique for significantly compressing fingerprint images, while retaining the necessary structural details for accurate identification. The paper presents a new algorithm for extracting the fundamental identification features for use in automated fingerprint matching systems. Contrary to existing algorithms, the method presented here is tested on compressed/decompressed fingerprint images. We show that using the compression algorithm proposed earlier by the author, we are able to obtain a valid description of the fingerprints at compression ratios down to 50:1.
A detailed analysis of photovoltaic panel hot spot phenomena based on the bishop model

In this paper, we introduce a detailed photovoltaic panel (PV) model that includes Bishop circuit representation for the hot spot phenomena. The hot spot phenomenon is considered as preliminary damage occurring in solar panels. The main reasons are shading and possibly dust accumulation. The hot spot reduces the output power of the panel and it life time. The developed model was based on Matlab /Simulink tool. The model simulation results confirm the shading rate control on the amount output power. The hot spot, current distribution detailed picture was reached under several testing conditions. The presented detailed has shown its capability to follow the switching action of the bypass diode during the event of shading in a panel string. The details about shunt current, string current and bypass diode current along with the corresponding power dissipation in a panel string are newly introduced in this work.
An optimal feature selection technique using the concept of mutual information

We present a mutual information-based technique to perform feature selection for the purpose of classification. The technique selects those features that have maximum mutual information with the specified classes. The best solution may be obtained through an exhaustive search (all possible combinations). However, even with a small number of features, this solution becomes impractical due to the exponentially increasing computational cost. Unlike other techniques that select features individually, our technique considers a trade off between computational cost and combined feature selection. Extensive experiments have shown that the proposed technique outperforms existing feature selection methods based on individual features.
A Novel Audio Coding Scheme Using Warped Linear Prediction Model and the Discrete Wavelet Transform

In this paper, we present a novel audio coder using the discrete wavelet transform (DWT) and warped linear prediction (WLP). In contrast to conventional LP, WLP allows for the control of frequency resolution to closely match the response of the human auditory system. The structure of the system is similar to the transform coded excitation techniques used in wideband speech coding, where LP has been replaced with WLP, and the residual is analyzed by a wavelet filterbank designed to approximate the critical bands. The inherent shaping of the WLP synthesis filter, and a controlled bit allocation to the wavelet coefficients helps minimise the perceptually significant noise due to the quantization error in the residual. For monophonic signals sampled at 44.1 kHz, the coder achieves near transparent to transparent quality for a variety of speech and music signals at an average bitrate of about 64 kb/s. Tests also show that the coder (in its initial implementation) delivers superior quality to the MPEG layer III and comparable quality to the MPEG2-AAC codec when operating at the same bitrate
Polynomial order reducing property of lattice filters for FM signals with polynomial phases

The behaviour of the optimal and adaptive reflection coefficients of lattice filters for FM signals with polynomial phases of order p is investigated. It is theoretically shown that the optimal reflection coefficients form FM signals with lower order polynomial phases p-1. This new characteristic which is correspondingly held for the adaptive reflection coefficients is introduced as the polynomial order reducing (POR) property of the reflection coefficients. Using the POR property, by inputting the signal produced by one adaptive coefficient to the next adaptive lattice filter, the input polynomial order can frequently be reduced so that a sinusoid is obtained. More interestingly, the phases of these signals are related to each other. While, the POR property is heuristically worthwhile, it may potentially be used in several signal processing applications. As an example, this property is applied to the instantaneous frequency (IF) estimation of a linear FM signal.
Hierarchical approach to formant detection and tracking through instantaneous frequency estimation

Formant frequencies, represented by major peaks in the spectrum of speech signals, convey important information about speech. The authors propose a method for detecting the formants of voiced speech through 'instantaneous frequency' (IF) estimation using a recursive least square (RLS) algorithm. The accuracy of the technique is assessed by comparing it with conventional formant detection techniques. This method is also analysed from the viewpoint of phonetic conformity using 'temporal decomposition'.
Wideband audio compression using wrapped linear prediction and the discrete wavelet transform

In this paper, an audio coder using the discrete wavelet transform (DWT) and a warped linear prediction (WP) model, is proposed. In contrast to conventional LP, WLP allows for the control of frequency resolution closely match the response of the human auditory system. The residual from the inverse WLP filtering is analyzed by a wavelet filterband designed to approximate the critical bands. For monophonic signals sampled at 44.1 KHz, the coder achieves near transparent quality at an average bit-rate of 64Kb/s.
Arabic sign language recognition by decisions fusion using Dempster-Shafer theory of evidence

Most sign language recognition systems that use gloves and hand trackers combine the data from both devices at the sensor level. In this paper we propose a new approach by combining information acquired from the gloves and the hand tracking systems at the decision level using the Dempster-Shafer theory of evidence. The results using the Dempster-Shafer on the recognition of 100 two-handed signs show enhanced performance compared to the individual systems and to classification based on combined features. A recognition accuracy of 84.7%, and 91.3% are achieved when attempting to recognize the signs from the hand tracker only, and the glove data, respectively. When the sensor data from the gloves and hand tracking systems are combined, a recognition accuracy of 96.2% was achieved while a recognition accuracy of 98.1% was achieved when the fusion is performed at the decision level using Dempster-Shafer theory of evidence.
A new strategy for spectroscopic imaging (magnetic resonance imaging)

A novel approach for reducing the imaging artifact in spectroscopic imaging or chemical shift imaging (CSI) is described. In order to reduce the ringing and maintain adequate SNR for the metabolites, the data points in high k-space are measured with one excitation or a few excitations, while those in the low k-space are sampled with many excitations. By virtue of this sampling scheme, the data contain more noise in the high spatial frequency region, and the direct Fourier transform reconstruction will generate a very noisy image. Therefore, a technique based on adaptive smoothing is developed for the reconstruction. From simulated and in vivo proton CSI experiments, it is concluded that the technique is robust and can be used for in vivo applications.<<ETX>>
Comparative study of different parameters for temporal decomposition based speech coding

Temporal decomposition (TD) is an effective technique to compress the spectral information of speech through orthogonalization of the matrix of spectral parameters leading to an efficient rate reduction in speech coding applications. The performance of TD is a function of the parameters used. Although "decomposition suitability" of a parameter set is typically defined on the basis of "phonetic relevance" criterion, it can not be directly used in speech coding. Instead, quality evaluation of reconstructed speech is more appropriate. In this paper, we extend our earlier work in this area and attempt to assess several "popular" spectral parameter sets from the viewpoint of decomposition suitability in very low bit-rate speech coding using parametric, perceptually-based spectral, and energy distance measures.
Mammographic image segmentation using a tissue-mixture model and Markov random fields

The introduction of imaging and anatomical models can be used to develop robust algorithms for mammographic image analysis. The key to the proposed technique is to recognise that at any site in the observed image, a combination of tissues is present. The relationship between the different tissues is represented by a statistical model which is dictated by the imaging system. Markov random fields are used to model the anatomical knowledge. The two models are combined into a Bayesian framework to segment the image and extract regions of interest. Results indicate that reliable and verifiable analysis techniques can be developed utilising physically justified models. Representing the mammographic images in the proposed framework is intended to be more suitable for interpretation by human specialists.