nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic load balancing in distributed heterogeneous systems under stationary and bursty traffics

A heterogeneous system consisting of a number of hosts connected by a local area network (LAN) is described. The focus is on load balancing and process migration policies. An algorithm that increases the output performance of the system using load balancing is proposed. The computer simulation is based on a sender-initiated load balancing scheme, and is tested for a number of decision thresholds. A wide range of transfer delays due to bandwidth is considered in the simulation. Another parameter that is considered in the scheme is the uncertainty of the queuing status of the remote nodes, due to the combined effects of the system dynamics and the nonzero time required in probing the queuing status of the remote stations. Results show that the balancing scheme is capable of improving the system performance over that of no balancing for a wide range of transfer delays.<<ETX>>
A new approach to low bit rate audio coding using a combined harmonic-multiband-wavelet representation

In this paper, we present updated results for the combined harmonic-wavelet based audio coder. This coder first approximates the audio frame as a sum of several sinusoids using a harmonic analysis-synthesis scheme. The reconstructed harmonic signal is then subtracted from the original to give a residual, which is analysed using a wavelet filtering scheme. After each step (harmonic analysis and wavelet filtering), the perceptually relevant parameters are quantised and encoded. The total least squares (TLS)-Prony algorithm is used for the harmonic analysis scheme, and cascaded M-band wavelet transforms are used for analysing the residual. At encoding bit rates of 60-70 kbit/s, this audio coder is capable of delivering better results than the MPEG layer II audio coder operating at the much higher bit rate of 128 kbit/s.
Classifiers Combination Techniques: A Comprehensive Review

In critical applications, such as medical diagnosis, security related systems, and so on, the cost or risk of action taking based on incorrect classification can be very high. Hence, combining expert opinions before taking decision can substantially increase the reliability of such systems. Such pattern recognition systems base their final decision on evidence collected from different classifiers. Such evidence can be of data type, feature type, or classifier type. Common problems in pattern recognition, such as curse of dimensionality, and small sample data size, among others, have also prompted researchers into seeking new approaches for combining evidences. This paper presents a criteria-based framework for multi-classifiers combination techniques and their areas of applications. The criteria discussed here include levels of combination, types of thresholding, adaptiveness of the combination, and ensemble-based approaches. The strengths and weaknesses of each of these categories are discussed in details. Following this analysis, we provide our perspective on the outlook of this area of research and open problems. The lack of a well-formulated theoretical framework for analyzing the performance of combination techniques is shown to provide a fertile ground for further research. In addition to summarizing the existing work, this paper also updates and complements the latest developments in this area of research.
A novel speaker identification algorithm using classifiers fusion

In this paper, a novel speaker identification technique using the Dempster-Shafer evidence theory is discussed. The objective is to fuse the complementary information present from different classifiers into a single decision. Here, we use a decreasing function of the distance (of the classifiers) as the belief function. We show that a combined classifier based on the Dempster-Shafer theory outperforms the individual LPCC and MFCC classifiers when used individually.
An improved model for speech excitation using time-frequency characterisation

This paper introduces a new method for approximating the excitation signal in a linear predictive coding (LPC) model over both time and frequency domains. In the frequency domain, a new two-band excitation model is proposed for decomposing the signal spectrum into two periodic and non-periodic components using the smoothed Wigner-Ville (SWV) distribution. This model provides high resolution in the time-frequency plane which gives accurate information about pitch and voicing. The periodic component is then analysed with a new "multi-pulse" like technique to approximate the signal in the time domain through repeating a representative pattern across each frame. It is shown that combining the spectral and the temporal models improves significantly over the performance of our earlier model for the excitation and gives a good approximation to the signal for most speech sounds. Evaluation results show that the proposed coding method operating at 2400 b/s outperforms current standard coders at similar rate.
On the performance of ensemble-based classifiers for Arabic speech recognition

Speech recognition continues to be a challenging research problem for diverse applications. The challenge is to develop better recognition systems, more robust, computationally more efficient, and versatile in nature. While western and eastern languages have attracted a lot of interest among researchers, the Arabic language, unfortunately, did not get an appropriate share of this interest. The Arabic language exhibits richness in semantics rarely found in other languages. To contribute to this field of work, we explore, in this paper, the aspect of combining evidences from multiple classifiers to improve accuracy of individual speech classification algorithms. The analysis covers fusion of evidence taken from different angles (perspectives) from statistical, to leaning, to evidence perspectives. Our experiments showed that ensemble-based classifiers achieve, on the average, an improvement in recognition accuracy of 4% or more, leading to overall recognition accuracies in the case of Arabic digits to more than 90%.
Robust human face detection in complex color images

We propose in this paper a model based technique for the detection of human faces from rich still color images. Traditionally, color images are represented in the RGB color space. RGB space, however, is not only a 3-dimensional space but also includes brightness or luminance which is not a reliable criterion for skin separation. To avoid the effect of luminance, we propose to work in the chromatic or pure color space. Using such space, a Gaussian model for the skin color pixels is developed and a skin likelihood image is obtained. Such image is then transformed into a binary image using adaptive thresholding. Finally, bright regions satisfying certain "facial" properties are obtained followed by a template matching stage. The method presented here is shown to provide robust detection under different environments and found to achieve very satisfactory results when compared to traditional "mug shot" based approaches.
Audio coding using the wavelet packet transform and a combined scalar-vector quantization

This paper investigates a hybrid scalar-vector quantization scheme for coding high quality audio signals. A wavelet packet transform (WPT) is used to decompose the audio signal into frequency bands slightly finer than the critical band divisions. A masking model computation is then used as input to the hybrid quantization scheme, where scalar quantization is used for coding the subbands from 0-5.5 kHz, and vector quantization is used for coding the subbands from 5.5-22 kHz. The performance of the proposed coder is assessed from segmental signal-to-noise ratios (SNR) and the perceived quality for a number of signals. The perceived quality is determined from informal comparisons between the uncoded signals at the original bitrate of 705 kb/s, and the same signals coded with (1) the proposed coder at 80 kb/s, (2) a coder using only scalar quantization at both 128 kb/s and 96 kb/s, and (3) the MPEG layer III coder at 64 kb/s. The comparisons indicate that a very good coder quality is possible with the proposed coder at bitrates of approximately 80 kb/s. This represents a saving of about 16 kb/s over full scalar quantization with a similar quality. Further bitrate reduction with the proposed coder is possible by entropy coding of the scalar quantized transform coefficients and the VQ indices.
2D-6 Automatic Detection of High Temperature Hydrogen Attack Defects from Ultrasonic A-Scan Signals

In this paper, an out-of-service pressure vessel known to have lots of high temperature hydrogen attach (HTHA) defects is used to develop in a cost effective manner a database of ultrasonic A-scan signals of this defect. Basic feature extraction method, coupled with traditional classifiers is shown to distinguish accurately the hydrogen attack defects from geometrically similar defects.
A robust technique for harmonic analysis of speech

A technique named least squares harmonic (LSH) is proposed for speech decomposition. The problem of harmonic estimation for speech is formulated as a solution to two sets of linear equations derived from minimising the mean squared error between original and estimated signals. The algorithm assumes that a good initial estimate of the pitch period is available. The performance of the algorithm is comparable to that of the total least square Prony method (TLSP) at high signal-to-noise ratios, however, at very low SNR, the proposed algorithm leads to a much more accurate harmonic representation. The approach used here has a great potential in coding and recognition applications.
A hybrid RFID-LPR system for vehicle access control during Pilgrimage season in Saudi Arabia

RFID (radio frequency identification) and license plate recognition (LPR) technologies have proven to be very effective in providing innovative solutions to numerous real world applications. In this paper, we propose an RFID-based system supported by license plate recognition (LPR) technology for monitoring and controlling entry of vehicles to the Holy region of Makkah (Saudi Arabia) during Hajj (Pilgrimage) season. Authorized vehicles are provided with passive RFID tags that are programmed to specify their schedule of entry. This is useful in allowing entry of service vehicles during low traffic periods of pilgrims' vehicles. The proposed LPR system is based on a two-stage license plate-character recognition algorithm. The license plate region is detected using morphological filtering while the character recognition stage uses a neural network approach. The extensive tests we conducted showed that the system is robust to different traffic conditions and is able to limit unauthorized vehicles from entry during Hajj period. A vehicle recognition accuracy of around 95% was achieved under normal conditions.
Analysis of multiple access interference in synchronous downlink CDMA systems with random signature sequences in AWGN and fading environments

In this work, exact analysis of the multiple access interference (MAI) for synchronous downlink CDMA systems is carried out for BPSK signals with random signature sequences in both additive white Gaussian (AWGN) noise and different fading environments, namely Nakagami and Rician. Two different scenarios are considered: one with equal transmitted powers while the other considers the case of unequal transmitted power. For both cases, the exact probability density function (pdf) of MAI plus noise is derived. Finally, a number of simulation results are presented to verify our theoretical derivations.
AR parameter estimation from noisy data using the EM algorithm

This paper considers the problem of parameter estimation of Gaussian autoregressive (AR) processes in the presence of additive white Gaussian noise. The proposed algorithm is based on formulating the estimation problem as an iterative expectation-maximisation (EM) procedure. The observations are seen as the 'incomplete' data and the set formed by the AR process and the noise process represents the 'complete' data. The algorithm is guaranteed to converge in the likelihood function of the parameters. The algorithm is easily generalised to other structures of the covariance matrix of the additive noise. Performance results show that the algorithm is successful in estimating the parameters even at very low signal-to-noise ratios (SNR).<<ETX>>
A hybrid LP-harmonics model for low bit-rate speech compression with natural quality

We present a new LP-harmonic based speech codec. At the coder the speech signal is pre-processed, and an LP analysis is carried out 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, and extract and classify the harmonies 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 for the LP filter is expressed 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 two versions of the coder operating at 1.82, and 2.56 kbit/s showed that the output speech has high intelligibility, with quality comparable to that of a 4 kbit/s sinusoidal codec.
A survey of image-based Arabic sign language recognition

Sign language is the native language of deaf and hearing impaired people which they prefer to use on their daily life. Few interpreters are available to facilitate communication between deaf and vocal people. However, this is neither practical nor possible for all situations. Advances in information technology encouraged the development of systems that can facilitate the automatic translation between sign language and spoken language, and thus removing barriers facing the integration of deaf people in the society. A lot of research has been carried on the development of systems that translate sign languages into spoken words and the reverse. However, only recently systems translating between Arabic sign language and spoken language have been developed. Many signs of the Arabic sign language are reflection of the environment (White color in Arabic sign language is a finger pointing to the chest of the signer as the tradition for male is to wear white color dress). Several review papers have been published on the automatic recognition of other sign languages. This paper represents the first attempt to review systems and methods for the image based automatic recognition of the Arabic sign language. It reviews most published papers and discusses a variety of recognition methods. Additionally, the paper highlights the main challenges characterizing the Arabic sign language as well as potential future research directions in this area.
A two-threshold cooperative spectrum sensing algorithm using swarm intelligence

In recent years, we have witnessed a rapid growth in wireless communications, especially in providing quality multimedia services. As a result, there is ever growing demand for radio spectrum. The present static frequency allocation cannot accommodate this demand as most of the spectrum is occupied by licensed users and is used inefficiently. Cognitive Radio (CR) has emerged as an effective technology to solve the inefficiency of spectrum usage by opportunistically accessing the under-utilized frequency bands. In this paper, we propose a Hybrid PSO-OR (Particle Swarm Optimization and OR) algorithm which uses a double threshold energy detector to perform spectrum sensing. The fusion centre, in the cognitive radio network, collects local decisions as well as energy values from secondary users. Particle Swarm Optimization (PSO) is then used to optimize the decision from the “fuzzy” secondary users. A final collective decision is made based on local decisions and observation values. This way, double thresholds will help in reducing the communication overhead over the reporting channel compared to single threshold data fusion techniques with a negligible loss in performance. Our results show that the proposed Hybrid PSO-OR algorithm outperforms the Hybrid EGC-OR algorithm.
Prototype Arabic Sign language recognition using multi-sensor data fusion of two leap motion controllers

Sign language is important for facilitating communication between hearing impaired and the rest of society. However, most vocal people do not understand sign language, hence, the need to develop system capable of translating sign language. Two approaches have traditionally been used in the literature: image-based and glove-based systems. Glove-based systems require the user to wear electronic gloves while performing the signs. The glove includes a number of sensors detecting different hand and finger articulations. Image-based systems use camera(s) to acquire a sequence of images of the hand. Each of the two approaches has its own disadvantages. The glove-based method is not natural as the user must wear a cumbersome instrument while the camera-based system requires specific background and environmental conditions to achieve high accuracy. In this paper, we propose a new approach for Arabic Sign Language Recognition (ArSLR) which involves the use of two Leap Motion Controllers (LMC) to prevent the case of one finger being occluded by another finger or hand. This device detects and tracks the hand and fingers to provide position and motion information. We propose to use the two LMCs as a backbone of the ArSLR system. In addition to data acquisition, the system includes a preprocessing stage, a feature extraction stage, and a classification stage. Fusion of evidences from the two LMCs at the feature extraction and classification stage was also investigated using Dempster-Shafer theory of evidence. Features fusion from the two LMCs gives 97.7% classification accuracy with Linear Discriminant Analysis (LDA) classifier and 97.1% with classifier level fusion. This gives better recognition over the use of a single LMC.
A new approach to modeling excitation in very low-rate speech coding

A new method for two-band approximation of excitation signals in an LPC model, to improve speech naturalness in very low rate coding, is proposed. Based on a simplified model of multi-band excitation, the method accurately determines the degree of periodicity, using the concept of instantaneous frequency (IF) estimation in the frequency domain. The harmonic structure in the spectrum of LPC residual, within individual bands, is identified based on flatness of the IF as a criterion for pitch and voicing detection. On this basis, the excitation is modelled by combining a predefined periodic signal in the lower band and a random signal in the higher band. It is shown that this improves considerably the naturalness of reconstructed speech in very low rate coding in comparison with that obtained using traditional binary excitation. The performance of the technique is also given in temporal decomposition (TD) based coding at 800 b/s.
A fast Geodesic Active Contour model for medical images segmentation using prior analysis

The deformable Geodesic Active Contour (GAC) method is one of the most important techniques used in object boundaries detection in images. In this work, we modify the automatic GAC technique by incorporating priori information extracted from the region of interest. We introduce a new stopping function to speed up convergence and improve accuracy. The proposed technique was applied to both synthetic and real medical images. We show an improvement in speed of more than 40% together with an excellent accuracy compared to the traditional GAC model.
A Distributed Principal Component Analysis Compression for Smart Seismic Acquisition Networks

This paper develops a new framework for data compression in seismic sensor networks by using the distributed principal component analysis (DPCA). The proposed DPCA scheme compresses all seismic traces in the network at the sensor level. First of all, the statistics of the seismic traces acquired at all sensors are represented by a mixture model of a number of probability density functions. Based on this mixture model, the DPCA finds the global PCs at the fusion center. These PCs are then sent back to all sensors so that each sensor projects its own traces over these PCs. This scheme does not require transmitting the original traces, here, leading to a low computational load and a high compression ratio, compared with compression obtained using the local PC analysis (LPCA). Furthermore, we develop an efficient communication solution for the DPCA implementation on practical sensor networks. Finally, the proposed scheme is evaluated using real and synthetic seismic data showing improved performance over the LPCA and the traditional 2-D discrete cosine transform (DCT-2-D) compression. Specifically, to preserve a given signal energy during the compression, the DPCA is shown to achieve a higher compression ratio than the LPCA and the DCT-2-D.
A novel pitch estimation technique using the Teager energy function

In speech processing, methods based on glottal closure instances (GCI) for pitch period estimation have proven to give good results. We propose here a new method to estimate the GCIs based on the Teager energy function (TEF). The technique is simple and has a low computational load. The method is based on the peak detection of the TEF for each frame. A smoothing technique is then implemented to pick the right value for the pitch period. Intensive experimental results showed that the proposed technique outperforms both the autocorrelation and cepstral based techniques.
Image based arabic sign language recognition

In this paper we propose an image based system for Arabic Sign Language recognition. The recognition stage is performed using a Hidden Markov Model. We have used a Gaussian skin color model to detect the signer’s face. The detected face region 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. Such features are then used as input to the HMM. The proposed system achieved a recognition accuracy of 98% for a data set of 50 signs.
A bitstream scalable audio coder using a hybrid WLPC-wavelet representation

In this paper, we present 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 is capable of being decoded at rates between 16 kbit/s and 80 kbit/s. As the bitrate increases, so too does the signal quality. At 80 kbit/s, 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 similar, but fixed, bitrates.